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Artículos de revistas sobre el tema "Video processing"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Jiang, Zhiying, Chong Guan, and Ivo L. de Haaij. "Congruity and processing fluency." Asia Pacific Journal of Marketing and Logistics 32, no. 5 (2019): 1070–88. http://dx.doi.org/10.1108/apjml-03-2019-0128.

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Purpose The purpose of this paper is to investigate the benefits of Ad-Video and Product-Video congruity for embedded online video advertising. A conceptual model is constructed to test how congruity between online advertisements, advertised products and online videos impact consumer post-viewing attitudes via processing fluency. Design/methodology/approach An online experiment with eight versions of mock video sections (with embedded online video advertisements) was conducted. The study is a 2 (type of appeal: informational vs emotional) × 2 (Ad-Video congruity: congruent vs incongruent) × 2

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2

Coutts, Maurice D., and Dennis L. Matthies. "Video disc processing." Journal of the Acoustical Society of America 81, no. 5 (1987): 1659. http://dx.doi.org/10.1121/1.395033.

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3

Uytterhoeven, G. "Digital video processing." Journal of Computational and Applied Mathematics 66, no. 1-2 (1996): N5—N6. http://dx.doi.org/10.1016/0377-0427(96)80474-2.

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4

Ritwik Baranwal. "Automatic Summarization of Cricket Highlights using Audio Processing." January 2021 7, no. 01 (2021): 48–53. http://dx.doi.org/10.46501/ijmtst070111.

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The problem of automatic excitement detection in cricket videos is considered and applied for highlight generation. This paper focuses on detecting exciting events in video using complementary information from the audio and video domains. First, a method of audio and video elements separation is proposed. Thereafter, the “level-of-excitement” is measured using features such as amplitude, and spectral center of gravity extracted from the commentators speech’s amplitude to decide the threshold. Our experiments using actual cricket videos show that these features are well correlated with human as

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5

Wira Widjanarko, Kresna, Krisna Aditya Herlambang, and Muhamad Abdul Karim. "Faster Video Processing Menggunakan Teknik Parallel Processing Dengan Library OpenCV." Jurnal Komunikasi, Sains dan Teknologi 1, no. 1 (2022): 10–18. http://dx.doi.org/10.61098/jkst.v1i1.2.

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Video yang diputar seringkali terlalu lama diproses, terutama aplikasi yang membutuhkan pemrosesan real-time, seperti pemrosesan aplikasi video webcam, oleh karena itu dilakukan pemrosesan paralel untuk mempercepat proses komputasi video. Penelitian ini membahas tentang pemrosesan paralel pada video agar proses komputasi video berjalan lebih cepat dibandingkan tanpa pemrosesan paralel. Pengujian dilakukan dengan dua jenis data: aliran video dari webcam laptop dan file video .mp4. Bahasa pemrograman yang digunakan dalam pengujian ini adalah Python dengan bantuan library OpenCV. Penelitian ini m

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6

Et. al., G. Megala,. "State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (2021): 1256–72. http://dx.doi.org/10.17762/turcomat.v12i5.1793.

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Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standard

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7

Лун, Сюй, Xu Long, Йан Йихуа, Yan Yihua, Чэн Цзюнь, and Cheng Jun. "Guided filtering for solar image/video processing." Solar-Terrestrial Physics 3, no. 2 (2017): 9–15. http://dx.doi.org/10.12737/stp-3220172.

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A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results pr

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8

P. Hima Chandana, R. Ragupathy, and D. Vivekananda Reddy. "Video Summarization Using Machine Learning Techniques: An Overview." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 2080–86. https://doi.org/10.47392/irjaem.2025.0326.

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Technological advancement is a persistent aspect in our lives, and it emerges and grows at an exponential rate. Every day, a large quantity of data is generated in the form of text, image, audio and video. To process large amounts of video data, like movies, social media data and Surveillance data, requires a large amount of storage. Therefore, minimizing these videos by processing only vital content, takes a long processing time. To extract the key content from the video, which is a time-consuming procedure for the viewer, the entire video must be watched to overcome such challenges, video su

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9

Moon, Nazmun Nessa, Imrus Salehin, Masuma Parvin, et al. "Natural language processing based advanced method of unnecessary video detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5411. http://dx.doi.org/10.11591/ijece.v11i6.pp5411-5419.

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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze

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10

Nazmun, Nessa Moon, Salehin Imrus, Parvin Masuma, et al. "Natural language processing based advanced method of unnecessary video detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5411–19. https://doi.org/10.11591/ijece.v11i6.pp5411-5419.

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In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare

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11

Wang, Wei Hua. "The Design and Implementation of a Video Image Acquisition System Based on VFW." Applied Mechanics and Materials 380-384 (August 2013): 3787–90. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3787.

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Along with the development of technologies on computer, electronic and communication, there are more and more applications of digital image acquisition and processing technology used in computer and portable systems, such as videophone, digital cameras, digital television, video monitoring, camera phones and video conferencing etc. Digitized image makes image digital transmit with high quality, which facilitates image retrieval, analysis, processing and storage. In applications such as video conferencing, it is a crucial premise to capture videos. So, in this paper, we mainly introduce the vid

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12

Beric, Aleksandar, Jef van Meerbergen, Gerard de Haan, and Ramanathan Sethuraman. "Memory-centric video processing." IEEE Transactions on Circuits and Systems for Video Technology 18, no. 4 (2008): 439–52. http://dx.doi.org/10.1109/tcsvt.2008.918775.

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13

He, Wenjia, Ibrahim Sabek, Yuze Lou, and Michael Cafarella. "PAINE Demo: Optimizing Video Selection Queries with Commonsense Knowledge." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3902–5. http://dx.doi.org/10.14778/3611540.3611581.

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Because video is becoming more popular and constitutes a major part of data collection, we have the need to process video selection queries --- selecting videos that contain target objects. However, a naïve scan of a video corpus without optimization would be extremely inefficient due to applying complex detectors to irrelevant videos. This demo presents Paine; a video query system that employs a novel index mechanism to optimize video selection queries via commonsense knowledge. Paine samples video frames to build an inexpensive lossy index, then leverages probabilistic models based on existi

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14

Merigot, Alain, and Alfredo Petrosino. "Parallel processing for image and video processing." Parallel Computing 34, no. 12 (2008): 693. http://dx.doi.org/10.1016/j.parco.2008.09.001.

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15

Yamashina, Masakazu. "Video Signal Processing LSI. Image Processing DSP." Journal of the Institute of Television Engineers of Japan 48, no. 1 (1994): 38–43. http://dx.doi.org/10.3169/itej1978.48.38.

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16

Kumar, K. Mohan. "Video Summarization Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 373–77. https://doi.org/10.22214/ijraset.2025.67254.

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This project presents a Video Summarization Tool that processes and summarizes videos by identifying and emphasizing objects of interest. Implemented with Python, OpenCV, and Tkinter, the tool has a simple graphical user interface for video processing. The main functionality is based on the YOLOv3-tiny object detection model, which is optimized for quicker processing. The model, which is trained using OpenCV's DNN module, finds objects in video frames and outlines them with rectangles. The application analyzes videos by bypassing frames and resampling them, storing processed frames in an outpu

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17

Kavitha S Patil,. "Digital Image and Video Processing: Algorithms and Applications." Journal of Electrical Systems 20, no. 3s (2024): 1390–96. http://dx.doi.org/10.52783/jes.1516.

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Many of the techniques that are used in digital image and video processing were developed in the 1960s at Bell Laboratories. These techniques have applications in a variety of fields, including medical imaging, videophone, character recognition, satellite imagery, and wire-photo standards conversion. Additional applications include enhancement of photographs or vidoes. The early stages of image and video processing were developed with the intention of enhancing the overall quality of the image or video. For the purpose of enhancing the visual effect of humans, it is intended for human beings.

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18

RAMESH, Dr G., and P. RAMYA. "Object Detection and Annotation in Videos Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44619.

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This project develops an advanced video processing system capable of detecting and annotating objects in video footage. By leveraging deep learning-based object detection models, the system identifies, tracks, and labels objects across video frames, ensuring accurate analysis over time. The implementation uses Flask as a web framework, OpenCV for video handling, and the YOLO model for efficient object detection. Users can upload videos, specify target objects, and receive annotated outputs with bounding boxes and confidence levels. The system supports real-time processing, enabling automated m

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19

Gambhir, Akshat, K. S. Boppaiah, M. Shruthi Subbaiah, Pooja M, and Kiran P. "Video Oculographic System using Real-Time Video Processing." International Journal of Computer Applications 119, no. 22 (2015): 15–18. http://dx.doi.org/10.5120/21368-4400.

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20

Et. al., R. P. Dahake,. "Face Recognition from Video using Threshold based Clustering." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (2021): 272–85. http://dx.doi.org/10.17762/turcomat.v12i1s.1768.

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Video processing has gained significant attention due to the rapid growth in video feed collected from a variety of domains. Face recognition and summary generation is gaining attention in the branch of video data processing. The recognition includes face identification from video frames and face authentication. The face authentication is nothing but labelling the faces. Face recognition strategies used in image processing techniques cannot be directly applied to video processing due to bulk data. The video processing techniques face multiple problems such as pose variation, expression variati

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21

Jeon, Eun-Seon, Dae-Il Kang, Chang-Bong Ban, and Seong-Yul Yang. "Implementation of Video Processing Module for Integrated Modular Avionics System." Journal of Korea Navigation Institute 18, no. 5 (2014): 437–44. http://dx.doi.org/10.12673/jant.2014.18.5.437.

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22

Yamaguchi, Futao, Hiroshi Sugawara, and Tetsuya Senda. "Video Signal Processing LSI. Recent Trends in Camera and Video Signal Processing LSIs." Journal of the Institute of Television Engineers of Japan 48, no. 1 (1994): 20–24. http://dx.doi.org/10.3169/itej1978.48.20.

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23

Espeland, Håvard, Håkon Kvale Stensland, Dag Haavi Finstad, and Pål Halvorsen. "Reducing Processing Demands for Multi-Rate Video Encoding." International Journal of Multimedia Data Engineering and Management 3, no. 2 (2012): 1–19. http://dx.doi.org/10.4018/jmdem.2012040101.

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Segmented adaptive HTTP streaming has become the de facto standard for video delivery over the Internet for its ability to scale video quality to the available network resources. Here, each video is encoded in multiple qualities, i.e., running the expensive encoding process for each quality layer. However, these operations consume both a lot of time and resources, and in this paper, the authors propose a system for reusing redundant steps in a video encoder to improve the multi-layer encoding pipeline. The idea is to have multiple outputs for each of the target bitrates and qualities where the

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24

Mashtalir, S. V., and D. P. Lendel. "DRONE VIDEO PROCESSING BY FRAGMENT ANALYSIS." System technologies 2, no. 157 (2025): 3–11. https://doi.org/10.34185/1562-9945-2-157-2025-01.

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This study focused on analyzing drone video to address the inherent complexity of processing videos captured with a moving camera. Each frame is divided into smaller fragments through a grid-based segmentation technique, enabling a localized and detailed motion analysis. Singular Value Decomposition (SVD) is applied to compute the Ki Fan norm for each fragment, enabling the detection of dynamic changes between consecutive frames. The fragment-level analysis allows the algorithm to robustly identify regions of interest and dif-ferentiate between global motion (camera movement) and local motion

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25

Shilpa, Bagade, Budati Anil Kumar, and L. Koteswara Rao. "Optimized Visual Internet of Things in Video Processing for Video Streaming." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 362–69. http://dx.doi.org/10.17762/ijritcc.v11i5s.7045.

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The global expansion of the Visual Internet of Things (VIoT) has enabled various new applications during the last decade through the interconnection of a wide range of devices and sensors.Frame freezing and buffering are the major artefacts in broad area of multimedia networking applications occurring due to significant packet loss and network congestion. Numerous studies have been carried out in order to understand the impact of packet loss on QoE for a wide range of applications. This paper improves the video streaming quality by using the proposed framework Lossy Video Transmission (LVT) fo

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26

Tseng, Shu-Ming, Zhi-Ting Yeh, Chia-Yang Wu, Jia-Bin Chang, and Mehdi Norouzi. "Video Scene Detection Using Transformer Encoding Linker Network (TELNet)." Sensors 23, no. 16 (2023): 7050. http://dx.doi.org/10.3390/s23167050.

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This paper introduces a transformer encoding linker network (TELNet) for automatically identifying scene boundaries in videos without prior knowledge of their structure. Videos consist of sequences of semantically related shots or chapters, and recognizing scene boundaries is crucial for various video processing tasks, including video summarization. TELNet utilizes a rolling window to scan through video shots, encoding their features extracted from a fine-tuned 3D CNN model (transformer encoder). By establishing links between video shots based on these encoded features (linker), TELNet efficie

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27

Kumaravel R, Kathirvel A, Hari Haran R, and Dhanasekaran M. "Video Summarization Tool Using Machine Learning." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 658–61. https://doi.org/10.47392/irjaeh.2025.0091.

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Video summarization is a crucial task in multimedia processing, allowing efficient content consumption by extracting the most relevant parts of a video. This research focuses on an automated video summarization system using machine learning techniques. The system integrates video processing, speech-to-text transcription, summarization, and translation. MoviePy is used for video extraction, Whisper for transcription, and the Hugging Face Transformers pipeline for text summarization. Google Trans is employed for multilingual support. The backend is developed using Django, while MongoDB serves as

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28

Elgamml, Mohamed M., Fazly S. Abas, and H. Ann Goh. "Semantic Analysis in Soccer Videos Using Support Vector Machine." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (2019): 2055018. http://dx.doi.org/10.1142/s0218001420550186.

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A tremendous increase in the video content uploaded on the internet has made it necessary for auto-recognition of videos in order to analyze, moderate or categorize certain content that can be accessed easily later on. Video analysis requires the study of proficient methodologies at the semantic level in order to address the issues such as occlusions, changes in illumination, noise, etc. This paper is aimed at the analysis of the soccer videos and semantic processing as an application in the video semantic analysis field. This study proposes a framework for automatically generating and annotat

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29

Vinayak Naik, Tadvidi. "Video Processing Based Smart Helmet." International Journal for Research in Applied Science and Engineering Technology V, no. IV (2017): 144–47. http://dx.doi.org/10.22214/ijraset.2017.4030.

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30

KAMIYA, KIYOSHI. "Image processing in video microscopy." Acta Histochemica et Cytochemica 24, no. 3 (1991): 353–56. http://dx.doi.org/10.1267/ahc.24.353.

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31

Daozheng Chen, M. Bilgic, L. Getoor, and D. Jacobs. "Dynamic Processing Allocation in Video." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 11 (2011): 2174–87. http://dx.doi.org/10.1109/tpami.2011.55.

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32

Desor, H. J. "Single-chip video processing system." IEEE Transactions on Consumer Electronics 37, no. 3 (1991): 182–89. http://dx.doi.org/10.1109/30.85511.

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33

Wadhwa, Neal, Michael Rubinstein, Frédo Durand, and William T. Freeman. "Phase-based video motion processing." ACM Transactions on Graphics 32, no. 4 (2013): 1–10. http://dx.doi.org/10.1145/2461912.2461966.

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34

Jia, Chuanmin, Haichuan Ma, Wenhan Yang, et al. "Video processing and compression technologies." Journal of Image and Graphics 26, no. 6 (2021): 1179–200. http://dx.doi.org/10.11834/jig.200861.

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35

Ahmad, Ishfaq, Yong He, and Ming L. Liou. "Video compression with parallel processing." Parallel Computing 28, no. 7-8 (2002): 1039–78. http://dx.doi.org/10.1016/s0167-8191(02)00100-x.

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36

Sony Corporation. "Motion-dependent video signal processing." Displays 14, no. 1 (1993): 60. http://dx.doi.org/10.1016/0141-9382(93)90023-x.

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Lin, Dennis, Xiaohuang Huang, Quang Nguyen, et al. "The parallelization of video processing." IEEE Signal Processing Magazine 26, no. 6 (2009): 103–12. http://dx.doi.org/10.1109/msp.2009.934116.

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Faroudja, Y., and N. Balram. "Video Processing for Pixellized Displays." SID Symposium Digest of Technical Papers 30, no. 1 (1999): 48. http://dx.doi.org/10.1889/1.1834064.

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Niwa, K., T. Araseki, and T. Nishitani. "Digital signal processing for video." IEEE Circuits and Devices Magazine 6, no. 1 (1990): 27–33. http://dx.doi.org/10.1109/101.47583.

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40

VIDAN, Cristian, Gavril ALEXANDRU, Razvan MIHAI, and Florin CATARGIU. "ON-BOARD UAV VIDEO PROCESSING FOR GROUND TARGET TRACKING." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 20 (June 18, 2018): 281–84. http://dx.doi.org/10.19062/2247-3173.2018.20.36.

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41

Kinugasa, T., A. Nishizawa, K. Koshio, T. Iguchi, J. Kamimura, and H. Marumori. "A video pre/post-processing LSI for video capture." IEEE Transactions on Consumer Electronics 42, no. 3 (1996): 776–80. http://dx.doi.org/10.1109/30.536184.

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42

Bilal A. Ozturk. "Independent Video Steganalysis Framework Perspective of Secure Video Processing." Journal of Electrical Systems 20, no. 6s (2024): 2541–49. http://dx.doi.org/10.52783/jes.3241.

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We have designed an algorithm of cross-domain feature set extraction for cross-domain video steganalysis of video steganography in multiple domains. For video steganography, we implemented the recently proposed steganography methods such as PMs, MVs, and IPMs. The outcomes of these techniques have fed as input to the proposed Steganalysis approach. We have designed a cross-domain technique for Steganalysis in which the global feature set is extracted according to common statistical properties. After the extraction of global features, the domain-specific features have been extracted in the loca

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43

Mochurad, Lesia. "A NEW APPROACH FOR TEXT RECOGNITION ON A VIDEO CARD." Computer systems and information technologies, no. 3 (September 28, 2022): 22–30. http://dx.doi.org/10.31891/csit-2022-3-3.

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An important task is to develop a computer system that can automatically read text content from images or videos with a complex background. Due to a large number of calculations, it is quite difficult to apply them in real-time. Therefore, the use of parallel and distributed computing in the development of real-time or near real-time systems is relevant. The latter is especially relevant in such areas as automation of video recording of traffic violations, text recognition, machine vision, fingerprint recognition, speech, and more. The paper proposes a new approach to text recognition on a vid

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44

XIAO, JIANGJIAN, HUI CHENG, FENG HAN, and HARPREET SAWHNEY. "GEO-BASED AERIAL SURVEILLANCE VIDEO PROCESSING FOR SCENE UNDERSTANDING AND OBJECT TRACKING." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 07 (2009): 1285–307. http://dx.doi.org/10.1142/s0218001409007582.

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This paper presents an approach to extract semantic layers from aerial surveillance videos for scene understanding and object tracking. The input videos are captured by low flying aerial platforms and typically consist of strong parallax from non-ground-plane structures as well as moving objects. Our approach leverages the geo-registration between video frames and reference images (such as those available from Terraserver and Google satellite imagery) to establish a unique geo-spatial coordinate system for pixels in the video. The geo-registration process enables Euclidean 3D reconstruction wi

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45

Shahadi, Haider Ismael, Zaid Jabbar Al-allaq, and Hayder Jawad Albattat. "Efficient denoising approach based eulerian videomagnification forcolour and motion variations." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4701. http://dx.doi.org/10.11591/ijece.v10i5.pp4701-4711.

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Digital video magnification is a computer-based microscope, which is useful to detect subtle changes to human eyes in recorded videos. This technology can be employed in several areas such as medical, biological, mechanical and physical applications. Eulerian is the most popular approach in video magnification. However, amplifying the subtle changes in video produces amplifying the subtle noise. This paper proposes an approach to reduce amplified noise in magnified video for both type of changes amplifications, color and motion. The proposed approach processes the resulted video from Eulerian

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46

Haider, Ismael Shahadi, Jabbar Al-allaq Zaid, and Jawad Albattat Hayder. "Efficient denoising approach based Eulerian video magnification for colour and motion variations." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 4701–11. https://doi.org/10.11591/ijece.v10i5.pp4701-4711.

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Digital video magnification is a computer-based microscope, which is useful to detect subtle changes to human eyes in recorded videos. This technology can be employed in several areas such as medical, biological, mechanical and physical applications. Eulerian is the most popular approach in video magnification. However, amplifying the subtle changes in video produces amplifying the subtle noise. This paper proposes an approach to reduce amplified noise in magnified video for both type of changes amplifications, color and motion. The proposed approach processes the resulted video from Eulerian

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47

M., Raja Suguna, Kalaivani A., and Anusuya S. "The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor." Symmetry 14, no. 10 (2022): 2041. http://dx.doi.org/10.3390/sym14102041.

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Aim: Advancements in multimedia technology have facilitated the uploading and processing of videos with substantial content. Automated tools and techniques help to manage vast volumes of video content. Video shot segmentation is the basic symmetry step underlying video processing techniques such as video indexing, content-based video retrieval, video summarization, and intelligent surveillance. Video shot boundary detection segments a video into temporal segments called shots and identifies the video frame in which a shot change occurs. The two types of shot transitions are cut and gradual. Il

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48

Grois, Dan, Evgeny Kaminsky, and Ofer Hadar. "Efficient Real-Time Video-in-Video Insertion into a Pre-Encoded Video Stream." ISRN Signal Processing 2011 (February 14, 2011): 1–11. http://dx.doi.org/10.5402/2011/975462.

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This work relates to the developing and implementing of an efficient method and system for the fast real-time Video-in-Video (ViV) insertion, thereby enabling efficiently inserting a video sequence into a predefined location within a pre-encoded video stream. The proposed method and system are based on dividing the video insertion process into two steps. The first step (i.e., the Video-in-Video Constrained Format (ViVCF) encoder) includes the modification of the conventional H.264/AVC video encoder to support the visual content insertion Constrained Format (CF), including generation of isolate

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49

A., D. Senthil Kumar, S. Anandhi T., and Muthu Ranganath. "PIXEL INTEGRATION TECHNIQUE FOR MULTI-IMAGE AND VIDEO COMPRESSION FOR DATA SECURITY." International Journal of Current Research and Modern Education 3, no. 1 (2018): 31–40. https://doi.org/10.5281/zenodo.1145484.

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This paper approaches security application for digital image and video processing. The techniques involve JPEG image and H.264 video compression, Simon cryptography algorithm using Verilog HDL followed by Image Interleaving, and last by Pixel Integration to generate integrated multi-image and video. The user can choose any of the image/ videos among the several integrated inputs displayed with a unique security. With the secure key assigned for each input, the original image or video is displayed by decrypting it from multiple image and videos.

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50

CH.Gopi, Hithaishi Gajulapalli, Mohitha Vinnakota, and Geenukunta Manish Kumar Goud. "Advanced Encryption For Quantum-Safe Video Transmission." International Journal of Information Technology and Computer Engineering 13, no. 2 (2025): 1207–13. https://doi.org/10.62647/ijitce2025v13i2pp1207-1213.

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This project enables secure video processing, encryption, and watermark embedding, focusing on user authentication, video encryption, and decryption capabilities. Users can register, log in, and upload videos along with watermarks for processing. Using the cryptography library, each uploaded video is encrypted, and its encryption key is split using Shamir's Secret Sharing, ensuring secure key distribution and storage. The encrypted frames are stored separately for later retrieval and decryption. Decryption occurs through reassembling key shares, allowing the original video to be reconstructed,

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