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Journal articles on the topic 'Image Encode'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Zhao, Anxin, Liang Li, and Shuai Liu. "UIDF-Net: Unsupervised Image Dehazing and Fusion Utilizing GAN and Encoder–Decoder." Journal of Imaging 10, no. 7 (2024): 164. http://dx.doi.org/10.3390/jimaging10070164.

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Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the encoder–decoder paradigm (UIDF-Net). This network leverages the Image Fusion Module (MDL-IFM) to fuse the features of dehazed images, producing clearer results. Additionally, to better extract haze information, we introduce a haze encoder (Mist-Encode) that effectively processes different frequency featu

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Narmatha, C., P. Manimegalai, and S. Manimurugan. "A Grayscale Image Hiding Encode Scheme for Secure Transmission." Current Signal Transduction Therapy 14, no. 2 (2019): 146–51. http://dx.doi.org/10.2174/1574362413666180802124040.

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Background: To transmit the secret data are in a secure manner or to prevent the intruder/ third party activities while transmitting secret data’s through the public networks are challenging task now. In order to deal with these situations, this paper presents an encryption/encoding technique of MSI (Modified Steganography for Image) for secret data before transmitting over the network. Methods: The MSI technique is classified into two phases, one is stegano image creation by encode process and another one is reconstructing the secret data from the stegano image by decode process. In encode pr

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Hu, Yu-Chen, Chia-Chen Lin, and Kang-Liang Chi. "Block Prediction Vector Quantization for Grayscale Image Compression." Fundamenta Informaticae 78, no. 2 (2007): 257–70. https://doi.org/10.3233/fun-2007-78204.

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This paper presents a new image compression scheme based on vector quantization (VQ) that exploits the inter-block and intra-block correlations in grayscale images. The similar block prediction technique is designed to encode the image blocks by their similar neighboring encoded blocks. Besides, two codebooks are used in the proposed scheme to exploit the intra-block correlation within each image block. Experimental results show that the proposed scheme not only provides good image qualities but also cuts down the required bit rates.

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Liu, Weihuang, Xiaodong Cun, Chi-Man Pun, Menghan Xia, Yong Zhang, and Jue Wang. "CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1746–54. http://dx.doi.org/10.1609/aaai.v37i2.25263.

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Image inpainting aims to fill the missing hole of the input. It is hard to solve this task efficiently when facing high-resolution images due to two reasons: (1) Large reception field needs to be handled for high-resolution image inpainting. (2) The general encoder and decoder network synthesizes many background pixels synchronously due to the form of the image matrix. In this paper, we try to break the above limitations for the first time thanks to the recent development of continuous implicit representation. In detail, we down-sample and encode the degraded image to produce the spatial-adapt

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Cui, Yuan, and Bo Nian Li. "A Multimedia System Based on OMAP3530." Applied Mechanics and Materials 40-41 (November 2010): 506–9. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.506.

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JPEG images decoded and encoded rapidly and effectively based on OMAP3530 chip. USB camera deployed as images’ acquisition equipment, and used the ARM + DSP multi-core OMAP3530 processor as image decode-encode processing chip. The results sent to the user interface ultimately. System’s development was based on DVSDK. The result proved system fast than others.

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Buhmann, Joachim M., Tilman Lange, and Ulrich Ramacher. "Image Segmentation by Networks of Spiking Neurons." Neural Computation 17, no. 5 (2005): 1010–31. http://dx.doi.org/10.1162/0899766053491913.

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A network of leaky integrate-and-fire (IAF) neurons is proposed to segment gray-scale images. The network architecture with local competition between neurons that encode segment assignments of image blocks is motivated by a histogram clustering approach to image segmentation. Lateral excitatory connections between neighboring image sites yield a local smoothing of segments. The mean firing rate of class membership neurons encodes the image segmentation. A weight modification scheme is proposed that estimates segment-specific prototypical histograms. The robustness properties of the network imp

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Truong Giang, Khang, Soohwan Song, and Sungho Jo. "TopicFM: Robust and Interpretable Topic-Assisted Feature Matching." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2447–55. http://dx.doi.org/10.1609/aaai.v37i2.25341.

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This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to such situations, most previous studies have attempted to encode the global contexts of a scene via graph neural networks or transformers. However, these contexts do not explicitly represent high-level contextual information, such as structural shapes or semantic instances; therefore, the encoded features are still not sufficiently discriminative in challenging scenes. We propose a novel image-matching method that applies a topic-modeling strategy to e

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Jain, Arpit, Chaman Verma, Neerendra Kumar, Maria Simona Raboaca, Jyoti Narayan Baliya, and George Suciu. "Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine." Information 14, no. 1 (2023): 29. http://dx.doi.org/10.3390/info14010029.

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The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training a

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Hara, Yuki, and Tomonori Kawano. "Run-Length Encoding Graphic Rules Applied to DNA-Coded Images and Animation Editable by Polymerase Chain Reactions." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 1 (2015): 5–10. http://dx.doi.org/10.20965/jaciii.2015.p0005.

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We previously proposed novel designs for artificial genes as media for storing digitally compressed image data, specifically for biocomputing by analogy to natural genes mainly used to encode proteins. A run-length encoding (RLE) rule had been applied in DNA-based image data processing, to form coding regions, and noncoding regions were created as space for designing biochemical editing. In the present study, we apply the RLE-based image-coding rule to creation of DNAbased animation. This article consisted of three parts: (i) a theoretical review of RLE-based image coding by DNA, (ii) a techni

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Li, Zhen, Changgen Peng, Weijie Tan, and Liangrong Li. "A Novel Chaos-Based Image Encryption Scheme by Using Randomly DNA Encode and Plaintext Related Permutation." Applied Sciences 10, no. 21 (2020): 7469. http://dx.doi.org/10.3390/app10217469.

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To ensure the security and privacy of digital image when its transmitting online or storing in the cloud, we proposed a novel chaos based image encryption scheme by using randomly DNA encode and plaintext related permutation. In our scheme, we first randomly encode plain image into a nucleotide sequence under the control by the piecewise linear chaotic map(PWLCM). After that, the plaintext related permutation would be done under the control sequence which generated by hyper chaotic Lorenz system (HCLS). Next, we make diffusion processing with key DNA sequence which is generated by another PWLC

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Jing, Yongcheng, Xiao Liu, Yukang Ding, et al. "Dynamic Instance Normalization for Arbitrary Style Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4369–76. http://dx.doi.org/10.1609/aaai.v34i04.5862.

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Prior normalization methods rely on affine transformations to produce arbitrary image style transfers, of which the parameters are computed in a pre-defined way. Such manually-defined nature eventually results in the high-cost and shared encoders for both style and content encoding, making style transfer systems cumbersome to be deployed in resource-constrained environments like on the mobile-terminal side. In this paper, we propose a new and generalized normalization module, termed as Dynamic Instance Normalization (DIN), that allows for flexible and more efficient arbitrary style transfers.

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PORTIANYI, IVAN, KAROLINA POSPIELOVA, and YURII OLIINYK. "ENCODING RASTER IMAGES BASED ON FRAGMENT SIMILARITY." Herald of Khmelnytskyi National University 303, no. 6 (2021): 73–80. http://dx.doi.org/10.31891/2307-5732-2021-303-6-73-80.

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This paper is devoted to image encoding based on determining the similarity of fragments by using neural networks to extract the features of fragments and machine learning algorithms to find similar fragments. In the modern world, the problem of image storage is quite relevant. Graphic data takes up quite a lot of disk space, while Internet users upload more and more pictures. Also, every year there is a development of photography and image quality is improving, respectively, and the size of graphic data is growing. Data warehouses of social networks, messengers, file sharers and other Interne

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Sun, Jun, Junbo Zhang, Xuesong Gao, et al. "Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks." Remote Sensing 14, no. 9 (2022): 1968. http://dx.doi.org/10.3390/rs14091968.

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In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges. Existing methods cannot extract spatial and spectral-channel contextual information in a targeted manner. In this paper, we propose an encoder–decoder network that fuses spatial attention and spectral-channel attention for HSI classification from three public HSI datasets to tackle these issues. In terms of feature information fusion, a multi-source attention mechanism including spatial and sp

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Chen, Rung-Ching, Pei-Yan Pai, Yung-Kuan Chan, and Chin-Chen Chang. "Lossless Image Compression Based on Multiple-Tables Arithmetic Coding." Mathematical Problems in Engineering 2009 (2009): 1–13. http://dx.doi.org/10.1155/2009/128317.

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This paper is intended to present a lossless image compression method based on multiple-tables arithmetic coding (MTAC) method to encode a gray-level imagef. First, the MTAC method employs a median edge detector (MED) to reduce the entropy rate off. The gray levels of two adjacent pixels in an image are usually similar. A base-switching transformation approach is then used to reduce the spatial redundancy of the image. The gray levels of some pixels in an image are more common than those of others. Finally, the arithmetic encoding method is applied to reduce the coding redundancy of the image.

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Xu, Jianing, Xin Xu, and Xiaoqiang Zhu. "Spread spectrum encode watermark algorithm in document images." Journal of Physics: Conference Series 2906, no. 1 (2024): 012016. https://doi.org/10.1088/1742-6596/2906/1/012016.

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Abstract This paper introduces a document image watermarking algorithm based on spread spectrum coding patterns. The algorithm firstly encodes the watermark information to be hidden using spread spectrum coding patterns to generate watermark code blocks. These watermark codes are characterized by their robustness against tampering, difficulty to forge, and ease of recognition. The watermark information is then embedded by fine-tuning the corresponding pixels in the B channel, achieving the imperceptibility of the watermark. During the decoding process, the document images are subjected to obta

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Sun, Yuanyuan, Rudan Xu, Lina Chen, and Xiaopeng Hu. "Image Retrieval Based on Fractal Dictionary Parameters." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/689602.

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Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

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Azhari, Ahmad, Candra Putra Negara, Azmi Badhi'uz Zaman, and Dimas Aji Setiawan. "Reconstruction Old Students Image Using The Autoencoder Method." Letters in Information Technology Education (LITE) 6, no. 1 (2023): 1. http://dx.doi.org/10.17977/um010v6i22023p1-5.

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Image Processing is image processing with a digital computer to produce new images according to the user's wishes. One implementation is to reconstruct the image. Through the extraction stages can get the characteristics of an image. The algorithm used is Adam Optimization, an extension of the stochastic gradient reduction that has seen wider adoption for deep learning applications in computer vision and natural language processing. In this study, we use the autoencoder technique, one variant of artificial neural networks generally used to "encode" data. The autoencoder is trained to produce t

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Fu, Xuhui. "Digital Image Art Style Transfer Algorithm Based on CycleGAN." Computational Intelligence and Neuroscience 2022 (January 13, 2022): 1–10. http://dx.doi.org/10.1155/2022/6075398.

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With the continuous development and popularization of artificial intelligence technology in recent years, the field of deep learning has also developed relatively rapidly. The application of deep learning technology has attracted attention in image detection, image recognition, image recoloring, and image artistic style transfer. Some image art style transfer techniques with deep learning as the core are also widely used. This article intends to create an image art style transfer algorithm to quickly realize the image art style transfer based on the generation of confrontation network. The pri

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Hu, Yu-Chen. "Predictive Grayscale Image Coding Scheme Using VQ and BTC." Fundamenta Informaticae 78, no. 2 (2007): 239–55. https://doi.org/10.3233/fun-2007-78203.

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A predictive image compression scheme that combines the advantages of vector quantization and moment preserving block truncation coding is introduced in this paper. To exploit the similarities among neighboring image blocks, the block prediction technique is employed in this scheme. If a similar compressed image block can be found in the neighborhood of current processing block, it is taken to encode this block. Otherwise, this image block is encoded either by vector quantization or moment preserving block truncation coding. A bit-rate reduced version of the proposed scheme is also introduced.

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Wu, Yung-Gi. "FAST FRACTAL IMAGE ENCODER DESIGN." SYNCHROINFO JOURNAL 7, no. 4 (2021): 40–44. http://dx.doi.org/10.36724/2664-066x-2021-7-4-40-44.

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Fractal theory has been widely applied in the filed of image compression due to the advantage of resolution independence, fast decoding, and high compression ratio. However, it has a fatal shortcoming of intolerant encoding time because that every range block is need to find its corresponding best matched domain block in the full image. Therefore, it has not been widely applied as other coding schemes in the field of image compression. In this paper, an algorithm is proposed to improve this time-consuming encoding drawback by the adaptive searching window, partial distortion elimination and ch

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Sharma, Sanjeev, Tarun Kumar, Ravi Dhaundiyal, Amit Kumar Mishra, Nitin Duklan, and Ashish Maithani. "Improved method for image security based on chaotic-shuffle and chaotic-diffusion algorithms." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 273–80. https://doi.org/10.11591/ijece.v9i1.pp273-280.

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In this paper, we propose to enhance the security performance of the color image encryption algorithm which depends on multi-chaotic systems. The current cryptosystem utilized a pixel-chaotic-shuffle system to encode images, in which the time of shuffling is autonomous to the plain-image. Thus, it neglects to the picked plaintext and known-plaintext attacks. Also, the statistical features of the cryptosystem are not up to the standard. Along these lines, the security changes are encircled to make the above attacks infeasible and upgrade the statistical features also. It is accomplished by alte

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Negara, Candra Putra, Azmi Badhi’uz Zaman, Dimas Aji Setiawan, and Ahmad Azhari. "Reconstruction Old Students Image Using The Autoencoder Method." Letters in Information Technology Education (LITE) 5, no. 2 (2022): 51. http://dx.doi.org/10.17977/um010v5i22022p51-54.

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Image Processing is image processing with a digital computer to produce new images according to the user's wishes. One implementation is to reconstruct the image. Through the extraction stages are able to get the characteristics of an image. The algorithm used is Adam Optimization, which is an extension of the stochastic gradient reduction that has just seen wider adoption for deep learning applications in computer vision and natural language processing. In this study using the autoencoder technique, which is one variant of artificial neural networks that are generally used to "encode" data. A

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Deep, G., J. Kaur, Simar Preet Singh, Soumya Ranjan Nayak, Manoj Kumar, and Sandeep Kautish. "MeQryEP: A Texture Based Descriptor for Biomedical Image Retrieval." Journal of Healthcare Engineering 2022 (April 11, 2022): 1–20. http://dx.doi.org/10.1155/2022/9505229.

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Image texture analysis is a dynamic area of research in computer vision and image processing, with applications ranging from medical image analysis to image segmentation to content-based image retrieval and beyond. “Quinary encoding on mesh patterns (MeQryEP)” is a new approach to extracting texture features for indexing and retrieval of biomedical images, which is implemented in this work. An extension of the previous study, this research investigates the use of local quinary patterns (LQP) on mesh patterns in three different orientations. To encode the gray scale relationship between the cen

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Yang, Xi, Jie Zhang, Han Fang, et al. "AutoStegaFont: Synthesizing Vector Fonts for Hiding Information in Documents." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3198–205. http://dx.doi.org/10.1609/aaai.v37i3.25425.

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Hiding information in text documents has been a hot topic recently, with the most typical schemes of utilizing fonts. By constructing several fonts with similar appearances, information can be effectively represented and embedded in documents. However, due to the unstructured characteristic, font vectors are more difficult to synthesize than font images. Existing methods mainly use handcrafted features to design the fonts manually, which is time-consuming and labor-intensive. Moreover, due to the diversity of fonts, handcrafted features are not generalizable to different fonts. Besides, in pra

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Ulacha, Grzegorz, and Mirosław Łazoryszczak. "Lossless Image Compression Using Context-Dependent Linear Prediction Based on Mean Absolute Error Minimization." Entropy 26, no. 12 (2024): 1115. https://doi.org/10.3390/e26121115.

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This paper presents a method for lossless compression of images with fast decoding time and the option to select encoder parameters for individual image characteristics to increase compression efficiency. The data modeling stage was based on linear and nonlinear prediction, which was complemented by a simple block for removing the context-dependent constant component. The prediction was based on the Iterative Reweighted Least Squares (IRLS) method which allowed the minimization of mean absolute error. Two-stage compression was used to encode prediction errors: an adaptive Golomb and a binary a

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Wei, Jingbo, Huan Zhou, Peng Ke, Yaobin Ma, and Rongxin Tang. "Sequential SAR-to-Optical Image Translation." Remote Sensing 17, no. 13 (2025): 2287. https://doi.org/10.3390/rs17132287.

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There is a common need for optical sequence images with high spatiotemporal resolution. As a solution, Synthetic Aperture Radar (SAR)-to-optical translation tends to bring high temporal continuity of optical images and low interpretation difficulty of SAR images. Existing studies have been focused on converting a single SAR image into a single optical image, failing to utilize the advantages of repeated observations from SAR satellites. To make full use of periodic SAR images, it is proposed to investigate the sequential SAR-to-optical translation, which represents the first effort in this top

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Ulutas, Mustafa. "Meaningful Share Generation for Increased Number of Secrets in Visual Secret-Sharing Scheme." Mathematical Problems in Engineering 2010 (2010): 1–18. http://dx.doi.org/10.1155/2010/593236.

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This paper presents a new scheme for hiding two halftone secret images into two meaningful shares created from halftone cover images. Meaningful shares are more desirable than noise-like (meaningless) shares in Visual Secret Sharing because they look natural and do not attract eavesdroppers' attention. Previous works in the field focus on either increasing number of secrets or creating meaningful shares for one secret image. The method outlined in this paper both increases the number of secrets and creates meaningful shares at the same time. While the contrast ratio of shares is equal to that

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Steinmetz, Peter N., Elaine Cabrales, Michael S. Wilson, et al. "Neurons in the human hippocampus and amygdala respond to both low- and high-level image properties." Journal of Neurophysiology 105, no. 6 (2011): 2874–84. http://dx.doi.org/10.1152/jn.00977.2010.

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A large number of studies have demonstrated that structures within the medial temporal lobe, such as the hippocampus, are intimately involved in declarative memory for objects and people. Although these items are abstractions of the visual scene, specific visual details can change the speed and accuracy of their recall. By recording from 415 neurons in the hippocampus and amygdala of human epilepsy patients as they viewed images drawn from 10 image categories, we showed that the firing rates of 8% of these neurons encode image illuminance and contrast, low-level properties not directly pertine

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VANISHREE, A. TWINKLE, and PINNAMRAJU T. S. . PRIYA. "Text-To-Image Generator Using Deeping Learning." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04863.

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Text-to-image generation is a transformative field in artificial intelligence that focuses on synthesizing realistic images from natural language descriptions. This paper explores the integration of diffusion models and transformer-based architectures to achieve high-quality, semantically aligned image generation from textual prompts. Diffusion models, known for their superior generative capabilities, gradually transform noise into images through a learned denoising process. Meanwhile, transformers, particularly pre-trained language and vision-language models like CLIP, are employed to underst

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Chaib, Souleyman, Dou El Kefel Mansouri, Ibrahim Omara, Ahmed Hagag, Sahraoui Dhelim, and Djamel Amar Bensaber. "On the Co-Selection of Vision Transformer Features and Images for Very High-Resolution Image Scene Classification." Remote Sensing 14, no. 22 (2022): 5817. http://dx.doi.org/10.3390/rs14225817.

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Recent developments in remote sensing technology have allowed us to observe the Earth with very high-resolution (VHR) images. VHR imagery scene classification is a challenging problem in the field of remote sensing. Vision transformer (ViT) models have achieved breakthrough results in image recognition tasks. However, transformer–encoder layers encode different levels of features, where the latest layer represents semantic information, in contrast to the earliest layers, which contain more detailed data but ignore the semantic information of an image scene. In this paper, a new deep framework

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Dwivedi, Vibhash. "Improved LSB Based Image Steganography Using Linked Pixel Technique: A Linked-List Inspired Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34005.

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Image steganography is the art of hiding data into images. Secret data such as messages, audio, images can be hidden inside the cover image. This is mainly achieved by hiding the data into the LSB (Least Significant Bit) of the image pixels. To improve the security of steganography, this paper introduces LPS (Least Significant Bit Plane Steganography), a novel approach inspired by linked lists, which diverges from traditional LSB techniques. LPS utilizes the LSB of each channel to encode both data and pointers to subsequent pixels, creating a linked-list-like structure within the image. Keywor

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Zhu, Junjie, Lin Gu, Xiaoxiao Wu, Zheng Li, Tatsuya Harada, and Yingying Zhu. "People Taking Photos That Faces Never Share: Privacy Protection and Fairness Enhancement from Camera to User." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14646–54. http://dx.doi.org/10.1609/aaai.v37i12.26712.

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The soaring number of personal mobile devices and public cameras poses a threat to fundamental human rights and ethical principles. For example, the stolen of private information such as face image by malicious third parties will lead to catastrophic consequences. By manipulating appearance of face in the image, most of existing protection algorithms are effective but irreversible. Here, we propose a practical and systematic solution to invertiblely protect face information in the full-process pipeline from camera to final users. Specifically, We design a novel lightweight Flow-based Face Encr

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Li, Shengyu, Xuesong Liu, Rongxin Jiang, Fan Zhou, and Yaowu Chen. "Dilated residual encode–decode networks for image denoising." Journal of Electronic Imaging 27, no. 06 (2018): 1. http://dx.doi.org/10.1117/1.jei.27.6.063005.

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Salih, Abdulkareem Mohammed, and Salih Hassan Mahmood. "Digital Color Image Watermarking Using Encoded Frequent Mark." Journal of Engineering 25, no. 3 (2019): 81–88. http://dx.doi.org/10.31026/j.eng.2019.03.07.

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With the increased development in digital media and communication, the need for methods to protection and security became very important factor, where the exchange and transmit date over communication channel led to make effort to protect these data from unauthentication access. This paper present a new method to protect color image from unauthentication access using watermarking. The watermarking algorithm hide the encoded mark image in frequency domain using Discrete Cosine Transform. The main principle of the algorithm is encode frequent mark in cover color image. The watermark image

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Lee, Jiann-Der, Yaw-Hwang Chiou, and Jing-Ming Guo. "Reversible Data Hiding Scheme with High Embedding Capacity Using Semi-Indicator-Free Strategy." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/476181.

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A novel reversible data-hiding scheme is proposed to embed secret data into a side-matched-vector-quantization- (SMVQ-) compressed image and achieve lossless reconstruction of a vector-quantization- (VQ-) compressed image. The rather random distributed histogram of a VQ-compressed image can be relocated to locations close to zero by SMVQ prediction. With this strategy, fewer bits can be utilized to encode SMVQ indices with very small values. Moreover, no indicator is required to encode these indices, which yields extrahiding space to hide secret data. Hence, high embedding capacity and low bit

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Kumaravel R, Shivakumaran S, Nanda Guru Pandiyan D, and Vijay Kumar R. "Colour Icon Matrix Bar-Code." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 02 (2025): 184–89. https://doi.org/10.47392/irjaeh.2025.0025.

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Cimbar-Code is a proof-of-concept 2D data encoding format designed to be like QR Codes, JAB codes, and Microsoft's HCCB. It works by encoding data in a grid of coloured symbols. Each tile in the grid can have one of sixteen symbols, and each symbol can have one of a few colours. This allows Cimbar-Code to encode a significant amount of data in a small space. Cimbar-Code is designed to be transmitted from a computer screen to a cell phone camera. The decoder app on the phone can then extract the data from the encoded image. This makes Cimbar-Code a potential solution for transferring data betwe

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Sadiq, B. J. S., V. Yu Tsviatkou, and M. N. Bobov. "Combined coding of bit planes of images." «System analysis and applied information science», no. 4 (December 30, 2019): 32–37. http://dx.doi.org/10.21122/2309-4923-2019-4-32-37.

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The aim of this work is to reduce the computational complexity of lossless compression in the spatial domain due to the combined coding (arithmetic and Run-Length Encoding) of a series of bits of bit planes. Known effective compression encoders separately encode the bit planes of the image or transform coefficients, which leads to an increase in computational complexity due to multiple processing of each pixel. The paper proposes the rules for combined coding and combined encoders for bit planes of pixel differences of images with a tunable and constant structure, which have lower computationa

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Alahmadi, Mohammad D. "Medical Image Segmentation with Learning Semantic and Global Contextual Representation." Diagnostics 12, no. 7 (2022): 1548. http://dx.doi.org/10.3390/diagnostics12071548.

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Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area. The U-Net model has become the standard design choice. Although the symmetrical structure of the U-Net model enables this network to encode rich semantic representation, the intrinsic locality of the CNN layers limits this network’s capability in modeling long-range contextual dependency. On the other hand, sequence to sequence Transformer models w

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Li, Ke, Xuncheng Wu, Weiwei Zhang, and Wangpengfei Yu. "Bird’s-Eye View Semantic Segmentation for Autonomous Driving through the Large Kernel Attention Encoder and Bilinear-Attention Transform Module." World Electric Vehicle Journal 14, no. 9 (2023): 239. http://dx.doi.org/10.3390/wevj14090239.

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Building an autonomous driving system requires a detailed and unified semantic representation from multiple cameras. The bird’s eye view (BEV) has demonstrated remarkable potential as a comprehensive and unified perspective. However, most current research focuses on innovating the view transform module, ignoring whether the crucial image encoder can construct long-range feature relationships. Hence, we redesign an image encoder with a large kernel attention mechanism to encode image features. Considering the performance gains obtained by the complex view transform module are insignificant, we

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Zhang, Jun Xing, and Chun Juan Bo. "Optical Surface Position Encode and Application Research." Advanced Materials Research 403-408 (November 2011): 1795–98. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.1795.

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A surface position location means based on encode recognition is presented; the design of a new kind of absolute position optical mouse using this technology is completed. The position encode algorithm and the code recognition method based on image acquisition and image process are presented also. The structure of pen-type sensor system and light path design of it are discussed. The coordinate conversation algorithm and the detected technology of pen down and pen up are provided. The advantages of the new kind of mouse are that it can write and draw more easily than traditional mouse.

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Xin, Gangtao, and Pingyi Fan. "Soft Compression for Lossless Image Coding Based on Shape Recognition." Entropy 23, no. 12 (2021): 1680. http://dx.doi.org/10.3390/e23121680.

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Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression

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Singh, Arvinder, Ninad Bhase, Manav Jain, and Tushar Ghorpade. "Machine Translation Systems for English Captions to Hindi Language Using Deep Learning." ITM Web of Conferences 44 (2022): 03004. http://dx.doi.org/10.1051/itmconf/20224403004.

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Machine Translation is the process of translating text from one language to another which helps to reduce the conversation gap among people from different cultural backgrounds. The task performed by the Machine Translation System is to automatically translate between pairs of different natural languages, where Neural Machine Translation System stands out from all because it provides fluent translation along with reasonable translation accuracy. The Convolution Neural Network encoder is used to find patterns in the images and encode it into a vector that is passed to the Long Short Term Memory

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N. Krishna Chaitanya. "Novel Method of Highly Secured Image Encryption Technique." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 460–71. https://doi.org/10.52783/jisem.v10i23s.3718.

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A major challenging issue in the today’s internet is the secure transmission of the images. There are methods that are been developed for secure transmission, but there is every possibility for acquiring the image and to change the content in the image. Most of the methods are complex and diesign is also very difficult. In this paper, we proposed a novel method for transmitting the image over the internet. The proposed method is based on original image to be sent, a cover image which is also called as reference image, three secret keys or passwords. It’s like a triple protection with the help

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Bashmal, Laila, Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Mansour Zuair, and Farid Melgani. "CapERA: Captioning Events in Aerial Videos." Remote Sensing 15, no. 8 (2023): 2139. http://dx.doi.org/10.3390/rs15082139.

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In this paper, we introduce the CapERA dataset, which upgrades the Event Recognition in Aerial Videos (ERA) dataset to aerial video captioning. The newly proposed dataset aims to advance visual–language-understanding tasks for UAV videos by providing each video with diverse textual descriptions. To build the dataset, 2864 aerial videos are manually annotated with a caption that includes information such as the main event, object, place, action, numbers, and time. More captions are automatically generated from the manual annotation to take into account as much as possible the variation in descr

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Li, Rumei, Liyan Zhang, Zun Wang, and Xiaojuan Li. "FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion." Sensors 24, no. 21 (2024): 7023. http://dx.doi.org/10.3390/s24217023.

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The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. To tackle these challenges, our novel FCSwinU network leverages the spectral fast Fourier convolution (SFFC) module for spectral feature extraction and utilizes the Swin T

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BAO, PAUL, and SUNG-WAI HONG. "IMAGE RESTORATION BASED ON GENERALIZED FINITE AUTOMATA ENCODED EDGE PRESERVING REGULARIZATION." International Journal of Image and Graphics 02, no. 03 (2002): 425–39. http://dx.doi.org/10.1142/s0219467802000731.

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We present an edge preserving regularization scheme for the restoration of degraded images compressed by the lossy compression method based on Generalized Finite Automata edge encoding and the Iterative Constrained Least Square Regularization technique. In this scheme, the degraded image reconstructed from lossy image compressions is treated as the input to the image restoration process. The edge information extracted from the source image is utilized as a priori knowledge for the subsequent reconstruction. In order to compromise the overall bit rate incurred by the additional edge information

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Rafea, S., and Dr N. H. Salman. "Hybrid DWT-DCT compression algorithm & a new flipping block with an adaptive RLE method for high medical image compression ratio." International Journal of Engineering & Technology 7, no. 4 (2018): 4602. http://dx.doi.org/10.14419/ijet.v7i4.25904.

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Huge number of medical images are generated and needs for more storage capacity and bandwidth for transferring over the networks. Hybrid DWT-DCT compression algorithm is applied to compress the medical images by exploiting the features of both techniques. Discrete Wavelet Transform (DWT) coding is applied to image YCbCr color model which decompose image bands into four subbands (LL, HL, LH and HH). The LL subband is transformed into low and high frequency components using Discrete Cosine Transform (DCT) to be quantize by scalar quantization that was applied on all image bands, the quantization

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YASEIN, MOHAMED S., and PAN AGATHOKLIS. "A WAVELET-BASED BLIND IMAGE DATA EMBEDDING ALGORITHM." Journal of Circuits, Systems and Computers 17, no. 01 (2008): 107–22. http://dx.doi.org/10.1142/s0218126608004198.

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In this paper, an algorithm for data embedding in images is proposed. The algorithm is based on using the Discrete Wavelet Transform domain to embed digital data into images. In order to increase the algorithm robustness and ensure the authentication of the data extracted, error detection/correction coding techniques are used to encode the embedded data. The proposed algorithm is blind, i.e., the embedded data are extracted and authenticated without any reference to the original image or data. Experimental results demonstrate the low perceptibility of distortions caused by the data embedding a

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Mohammed, Sajaa G., Safa S. Abdul-Jabbar, and Faisel G. Mohammed. "Art Image Compression Based on Lossless LZW Hashing Ciphering Algorithm." Journal of Physics: Conference Series 2114, no. 1 (2021): 012080. http://dx.doi.org/10.1088/1742-6596/2114/1/012080.

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Abstract Color image compression is a good way to encode digital images by decreasing the number of bits wanted to supply the image. The main objective is to reduce storage space, reduce transportation costs and maintain good quality. In current research work, a simple effective methodology is proposed for the purpose of compressing color art digital images and obtaining a low bit rate by compressing the matrix resulting from the scalar quantization process (reducing the number of bits from 24 to 8 bits) using displacement coding and then compressing the remainder using the Mabel ZF algorithm

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Zhang, Xinjie, Shenyuan Gao, Zhening Liu, et al. "CAMSIC: Content-aware Masked Image Modeling Transformer for Stereo Image Compression." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10239–47. https://doi.org/10.1609/aaai.v39i10.33111.

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Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations. However, those entropy models struggle to effectively capture the spatial-disparity characteristics inherent in stereo images, which leads to suboptimal rate-distortion results. In this paper, we propose a stereo image compression framework, named CAMSIC. CAMSIC independently transforms each image to latent representation and employs a powerful decoder-free Transformer entropy model to capture both spatial and disparity depe

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