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Articles de revues sur le sujet « Video quality prediction »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 1 février 2022

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1

Liu, Yu-xin, Ragip Kurceren et Udit Budhia. « Video classification for video quality prediction ». Journal of Zhejiang University-SCIENCE A 7, no 5 (mai 2006) : 919–26. http://dx.doi.org/10.1631/jzus.2006.a0919.

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Saad, Michele A., Alan C. Bovik et Christophe Charrier. « Blind Prediction of Natural Video Quality ». IEEE Transactions on Image Processing 23, no 3 (mars 2014) : 1352–65. http://dx.doi.org/10.1109/tip.2014.2299154.

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Anegekuh, Louis, Lingfen Sun et Emmanuel Ifeachor. « Encoding and video content based HEVC video quality prediction ». Multimedia Tools and Applications 74, no 11 (22 décembre 2013) : 3715–38. http://dx.doi.org/10.1007/s11042-013-1795-z.

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Barkowsky, Marcus, Iñigo Sedano, Kjell Brunnström, Mikołaj Leszczuk et Nicolas Staelens. « Hybrid video quality prediction : reviewing video quality measurement for widening application scope ». Multimedia Tools and Applications 74, no 2 (24 avril 2014) : 323–43. http://dx.doi.org/10.1007/s11042-014-1978-2.

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Hewage, C. T. E. R., S. T. Worrall, S. Dogan et A. M. Kondoz. « Prediction of stereoscopic video quality using objective quality models of 2-D video ». Electronics Letters 44, no 16 (2008) : 963. http://dx.doi.org/10.1049/el:20081562.

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Chen, Li-Heng, Christos G. Bampis, Zhi Li, Joel Sole et Alan C. Bovik. « Perceptual Video Quality Prediction Emphasizing Chroma Distortions ». IEEE Transactions on Image Processing 30 (2021) : 1408–22. http://dx.doi.org/10.1109/tip.2020.3043127.

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Khan, Asiya, Lingfen Sun, Emmanuel Ifeachor, Jose-Oscar Fajardo, Fidel Liberal et Harilaos Koumaras. « Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks ». International Journal of Digital Multimedia Broadcasting 2010 (2010) : 1–17. http://dx.doi.org/10.1155/2010/608138.

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The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.
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Koumaras, Harilaos, C. H. Lin, C. K. Shieh et Anastasios Kourtis. « A framework for end-to-end video quality prediction of MPEG video ». Journal of Visual Communication and Image Representation 21, no 2 (février 2010) : 139–54. http://dx.doi.org/10.1016/j.jvcir.2009.07.005.

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Anegekuh, Louis, Lingfen Sun, Emmanuel Jammeh, Is-Haka Mkwawa et Emmanuel Ifeachor. « Content-Based Video Quality Prediction for HEVC Encoded Videos Streamed Over Packet Networks ». IEEE Transactions on Multimedia 17, no 8 (août 2015) : 1323–34. http://dx.doi.org/10.1109/tmm.2015.2444098.

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Stojanović, Nenad, Boban Bondžulić, Boban Pavlović, Marko Novčić et Dimitrije Bujaković. « Improving the Prediction Accuracy of Objective Video Quality Evaluation ». Acta Polytechnica Hungarica 17, no 7 (2020) : 219–32. http://dx.doi.org/10.12700/aph.17.7.2020.7.12.

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Wang, Dayong, Yu Sun, Mingze Bai, Hongjian Li, Chun Yuan et Yuanyuan Huang. « Fast Intra prediction algorithm for quality scalable video coding ». Signal, Image and Video Processing 10, no 4 (21 juin 2015) : 625–32. http://dx.doi.org/10.1007/s11760-015-0786-0.

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Biernacki, Arkadiusz. « Traffic prediction methods for quality improvement of adaptive video ». Multimedia Systems 24, no 5 (25 novembre 2017) : 531–47. http://dx.doi.org/10.1007/s00530-017-0574-5.

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Liang, Xiaoyun, Zhaohong Li, Zhonghao Li et Zhenzhen Zhang. « Fake Bitrate Detection of HEVC Videos Based on Prediction Process ». Symmetry 11, no 7 (15 juillet 2019) : 918. http://dx.doi.org/10.3390/sym11070918.

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In order to defraud click-through rate, some merchants recompress the low-bitrate video to a high-bitrate one without improving the video quality. This behavior deceives viewers and wastes network resources. Therefore, a stable algorithm that detects fake bitrate videos is urgently needed. High-Efficiency Video Coding (HEVC) is a worldwide popular video coding standard. Hence, in this paper, a robust algorithm is proposed to detect HEVC fake bitrate videos. Firstly, five effective feature sets are extracted from the prediction process of HEVC, including Coding Unit of I-picture/P-picture partitioning modes, Prediction Unit of I-picture/P-picture partitioning modes, Intra Prediction Modes of I-picture. Secondly, feature concatenation is adopted to enhance the expressiveness and improve the effectiveness of the features. Finally, five single feature sets and three concatenate feature sets are separately sent to the support vector machine for modeling and testing. The performance of the proposed algorithm is compared with state-of-the-art algorithms on HEVC videos of various resolutions and fake bitrates. The results show that the proposed algorithm can not only can better detect HEVC fake bitrate videos, but also has strong robustness against frame deletion, copy-paste, and shifted Group of Picture structure attacks.
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Song, Jiarun, et Fuzheng Yang. « No-Reference Video Quality Assessment Model for Distortion Caused by Packet Loss in the Real-Time Mobile Video Services ». Advances in Multimedia 2014 (2014) : 1–15. http://dx.doi.org/10.1155/2014/606493.

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Packet loss will make severe errors due to the corruption of related video data. For most video streams, because the predictive coding structures are employed, the transmission errors in one frame will not only cause decoding failure of itself at the receiver side, but also propagate to its subsequent frames along the motion prediction path, which will bring a significant degradation of end-to-end video quality. To quantify the effects of packet loss on video quality, a no-reference objective quality assessment model is presented in this paper. Considering the fact that the degradation of video quality significantly relies on the video content, the temporal complexity is estimated to reflect the varying characteristic of video content, using the macroblocks with different motion activities in each frame. Then, the quality of the frame affected by the reference frame loss, by error propagation, or by both of them is evaluated, respectively. Utilizing a two-level temporal pooling scheme, the video quality is finally obtained. Extensive experimental results show that the video quality estimated by the proposed method matches well with the subjective quality.
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Fan, Kun, Chungin Joung et Seungjun Baek. « Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations ». Applied Sciences 10, no 22 (23 novembre 2020) : 8288. http://dx.doi.org/10.3390/app10228288.

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Video prediction which maps a sequence of past video frames into realistic future video frames is a challenging task because it is difficult to generate realistic frames and model the coherent relationship between consecutive video frames. In this paper, we propose a hierarchical sequence-to-sequence prediction approach to address this challenge. We present an end-to-end trainable architecture in which the frame generator automatically encodes input frames into different levels of latent Convolutional Neural Network (CNN) features, and then recursively generates future frames conditioned on the estimated hierarchical CNN features and previous prediction. Our design is intended to automatically learn hierarchical representations of video and their temporal dynamics. Convolutional Long Short-Term Memory (ConvLSTM) is used in combination with skip connections so as to separately capture the sequential structures of multiple levels of hierarchy of features. We adopt Scheduled Sampling for training our recurrent network in order to facilitate convergence and to produce high-quality sequence predictions. We evaluate our method on the Bouncing Balls, Moving MNIST, and KTH human action dataset, and report favorable results as compared to existing methods.
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Celenk, Özge, Thomas Bauschert et Marcus Eckert. « Machine Learning based KPI Monitoring of Video Streaming Traffic for QoE Estimation ». ACM SIGMETRICS Performance Evaluation Review 48, no 4 (17 mai 2021) : 33–36. http://dx.doi.org/10.1145/3466826.3466839.

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Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.
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17

Wang, Yongfang, Kanghua Zhu, Jian Wu et Yun Zhu. « Content aware video quality prediction model for HEVC encoded bitstream ». Multimedia Tools and Applications 76, no 18 (27 mars 2017) : 19191–209. http://dx.doi.org/10.1007/s11042-017-4574-4.

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Shi, Wenjuan, Yanjing Sun et Jinqiu Pan. « Continuous Prediction for Quality of Experience in Wireless Video Streaming ». IEEE Access 7 (2019) : 70343–54. http://dx.doi.org/10.1109/access.2019.2919610.

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19

Ebenezer, Joshua Peter, Zaixi Shang, Yongjun Wu, Hai Wei, Sriram Sethuraman et Alan C. Bovik. « ChipQA : No-Reference Video Quality Prediction via Space-Time Chips ». IEEE Transactions on Image Processing 30 (2021) : 8059–74. http://dx.doi.org/10.1109/tip.2021.3112055.

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Erabadda, Buddhiprabha, Thanuja Mallikarachchi, Chaminda Hewage et Anil Fernando. « Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-prediction ». Future Internet 11, no 8 (11 août 2019) : 175. http://dx.doi.org/10.3390/fi11080175.

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The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.
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Loh, Frank, Fabian Poignée, Florian Wamser, Ferdinand Leidinger et Tobias Hoßfeld. « Uplink vs. Downlink : Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming ». Sensors 21, no 12 (17 juin 2021) : 4172. http://dx.doi.org/10.3390/s21124172.

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Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.
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22

Ramachandra Rao, Rakesh Rao, Steve Göring et Alexander Raake. « Enhancement of Pixel-based Video Quality Models using Meta-data ». Electronic Imaging 2021, no 9 (18 janvier 2021) : 264–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-264.

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Current state-of-the-art pixel-based video quality models for 4K resolution do not have access to explicit meta information such as resolution and framerate and may not include implicit or explicit features that model the related effects on perceived video quality. In this paper, we propose a meta concept to extend state-of-the-art pixel-based models and develop hybrid models incorporating meta-data such as framerate and resolution. Our general approach uses machine learning to incooperate the meta-data to the overall video quality prediction. To this aim, in our study, we evaluate various machine learning approaches such as SVR, random forest, and extreme gradient boosting trees in terms of their suitability for hybrid model development. We use VMAF to demonstrate the validity of the meta-information concept. Our approach was tested on the publicly available AVT-VQDB-UHD-1 dataset. We are able to show an increase in the prediction accuracy for the hybrid models in comparison with the prediction accuracy of the underlying pixel-based model. While the proof-of-concept is applied to VMAF, it can also be used with other pixel-based models.
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Zheng, Hongyun, Yongxiang Zhao, Xi Lu et Rongzhen Cao. « A Mobile Fog Computing-Assisted DASH QoE Prediction Scheme ». Wireless Communications and Mobile Computing 2018 (28 août 2018) : 1–10. http://dx.doi.org/10.1155/2018/6283957.

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Video service has become a killer application for mobile terminals. For providing such services, most of the traffic is carried by the Dynamic Adaptive Streaming over HTTP (DASH) technique. The key to improve video quality perceived by users, i.e., Quality of Experience (QoE), is to effectively characterize it by using measured data. There have been many literatures that studied this issue. Some existing solutions use probe mechanism at client/server, which, however, are not applicable to network operator. Some other solutions, which aimed to predict QoE by deep packet parsing, cannot work properly as more and more video traffic is encrypted. In this paper, we propose a fog-assisted real-time QoE prediction scheme, which can predict the QoE of DASH-supported video streaming using fog nodes. Neither client/server participations nor deep packet parsing at network equipment is needed, which makes this scheme easy to deploy. Experimental results show that this scheme can accurately detect QoE with high accuracy even when the video traffic is encrypted.
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Zhou, Chao, Can Chen, Fei Ding et Dengyin Zhang. « Distributed Compressive Video Sensing with Mixed Multihypothesis Prediction ». Mathematical Problems in Engineering 2018 (7 novembre 2018) : 1–10. http://dx.doi.org/10.1155/2018/7020828.

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Traditional video acquisition systems require complex data compression at the encoder, which makes them unacceptable for resource-limited applications such as wireless multimedia sensor networks (WMSNs). To address this problem, distributed compressive video sensing (DCVS) represents a novel sensing approach with a simple encoder. This method shifts the computational burden from the encoder to the decoder and needs a robust reconstruction algorithm. In this paper, a mixed measurement-based multihypothesis (MH) reconstruction algorithm (mixed-MH) is proposed for DCVS to improve the reconstruction quality at low sampling rates. Considering the inaccuracy of MH prediction when measurements are insufficient, the available side information (SI) is resampled to obtain the artificial measurements, which are then integrated into real measurements via regularization. Furthermore, to avoid the negative effect of SI at high sampling rates, an adaptive regularization parameter is designed to balance the contributions of real and artificial measurements at different sampling rates. The experimental results demonstrate that the proposed mixed-MH prediction scheme outperforms other state-of-the-art algorithms in the reconstruction quality at the same low sampling rate.
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Xu, Peng, Man Guo, Lei Chen, Weifeng Hu, Qingshan Chen et Yujun Li. « No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning ». Complexity 2021 (28 janvier 2021) : 1–14. http://dx.doi.org/10.1155/2021/8834652.

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Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.
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Zhu, Hong Gao, et Qi Feng. « Network Video Transmission Quality Assurance Algorithm Based on Congestion Control ». Applied Mechanics and Materials 347-350 (août 2013) : 849–53. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.849.

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Time delay and packet loss rate during the network video transmission are the two important factors in network video transmission quality. The purpose of congestion control is to reduce the bad impact of network video transmission quality caused by time delay and packet loss rate. This paper proposed a network video transmission quality assurance algorithm based on unbalanced multiple descriptions coding scheme. The new algorithm predicts the congestion state of network by means of Markov model based on available bandwidth detection, changes video transmission path on the basis of the prediction. Experiments show that compared with RED (Random Early Detection) algorithm, the algorithm is much more effective to estimate network congestionreduce video packet loss rate and time delay, thus the network video transmission quality can be ensured more effectively.
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Liu, Yong, et Dawen Xu. « HEVC Information-Hiding Algorithm Based on Intra-Prediction and Matrix Coding ». International Journal of Digital Crime and Forensics 13, no 6 (novembre 2021) : 1–15. http://dx.doi.org/10.4018/ijdcf.20211101.oa11.

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Aiming at the problem that the data hiding algorithm of high efficiency video coding (HEVC) has great influence on the video bit rate and visual quality, an information hiding algorithm based on intra prediction mode and matrix coding is proposed. Firstly, 8 prediction modes are selected from 4×4 luminance blocks in I frame to embed the hidden information. Then, the Least Significant Bit (LSB) algorithm is used to modulate the LSB of the last prediction mode. Finally, the modulated luminance block is re-encoded to embed 4 bits secret information. Experimental results show that the algorithm improves the embedding capacity, guarantees the subjective and objective quality of the video, and the bit rate increases by 1.14% on average.
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Lee, Jin Young, Cheonshik Kim et Ching-Nung Yang. « Reversible Data Hiding Using Inter-Component Prediction in Multiview Video Plus Depth ». Electronics 8, no 5 (9 mai 2019) : 514. http://dx.doi.org/10.3390/electronics8050514.

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With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity.
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El Asmi, Sadok, Tasnim Abar et Asma Ben Letaifa. « Quality of experience prediction model for video streaming in SDN networks ». International Journal of Wireless and Mobile Computing 18, no 1 (2020) : 59. http://dx.doi.org/10.1504/ijwmc.2020.10026459.

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Abar, Tasnim, Asma Ben Letaifa et Sadok El Asmi. « Quality of experience prediction model for video streaming in SDN networks ». International Journal of Wireless and Mobile Computing 18, no 1 (2020) : 59. http://dx.doi.org/10.1504/ijwmc.2020.104769.

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Alreshoodi, Mohammed, Emad Danish, John Woods, Anil Fernando et Chamitha De Alwis. « Prediction of perceptual quality for mobile video using fuzzy inference systems ». IEEE Transactions on Consumer Electronics 61, no 4 (novembre 2015) : 546–54. http://dx.doi.org/10.1109/tce.2015.7389811.

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Pal, Debajyoti, et Vajirasak Vanijja. « A No-Reference Modular Video Quality Prediction Model for H.265/HEVC and VP9 Codecs on a Mobile Device ». Advances in Multimedia 2017 (2017) : 1–19. http://dx.doi.org/10.1155/2017/8317590.

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We propose a modular no-reference video quality prediction model for videos that are encoded with H.265/HEVC and VP9 codecs and viewed on mobile devices. The impairments which can affect video transmission are classified into two broad types depending upon which layer of the TCP/IP model they originated from. Impairments from the network layer are called the network QoS factors, while those from the application layer are called the application/payload QoS factors. Initially we treat the network and application QoS factors separately and find out the 1 : 1 relationship between the respective QoS factors and the corresponding perceived video quality or QoE. The mapping from the QoS to the QoE domain is based upon a decision variable that gives an optimal performance. Next, across each group we choose multiple QoS factors and find out the QoE for such multifactor impaired videos by using an additive, multiplicative, and regressive approach. We refer to these as the integrated network and application QoE, respectively. At the end, we use a multiple regression approach to combine the network and application QoE for building the final model. We also use an Artificial Neural Network approach for building the model and compare its performance with the regressive approach.
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Gu, Ming Ming, et Qi Jing. « Compressed Sensing with Generalized Hebbian Algorithm in Video Frame Prediction ». Applied Mechanics and Materials 397-400 (septembre 2013) : 2167–70. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2167.

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Compressed Sensing with Generalized Hebbian Algorithm (GHA) in Video Frame Prediction is proposed in the paper. After analyzing the inter-frame correlation among the images of video sequences, GHA, as neural network algorithm of PCA, is adopted to remove the transform coefficients with lower value according in order to implement video compressed sensing. Furthermore, for statistics of the adjacent frames are similar enough, the algorithm processes superiority in video frame prediction. Simulation results show that, the proposed algorithm can not only improve the reconstructed quality and the visual effects of the video sequence, but also save the sampling resources. Moreover, video frames can be predicted capitally through the application of the algorithm.
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Kalpana, Bapana, et Rangarajan Parthasarathy. « Real-time Dash Streaming Architecture for Internet of Things Using FBMRWP Model for Medical Videos ». Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no 8 (27 septembre 2019) : 761–68. http://dx.doi.org/10.2174/1573405614666180322142358.

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Background: The proposed method uses random adjustments in the online video quality based on the bandwidth allocated over Dynamic Adaptive Streaming over HTTP (DASH) streaming service. Aim: The main objective is to improve the video quality from DASH-HTTP servers with variable bandwidth. Here, the system is adjusted dynamically for providing best video quality services based on the requirement of the user. Methods: In order to achieve such objective, the DASH service is assigned with three modules. Initially, the quality is adjusted dynamically using Fractional Brownian Motion and Random Waypoint Mobility (FBM-RWP) model. This initial model schedules the packets in sub-streams based on the priority as per the requirement of the user. The final model uses the Proportional Integral Derivative (PID) quality control algorithm for the past and future prediction of quality based on bandwidth allocation. This feedback of quality is used by the FBM-RWP model to prioritize the packets in the sub-streams. The entire process works by matching the bit rate of video streaming with the user required quality. Results: The technique concentrates mostly on medical videos for improving the live video streaming in case of medical emergencies. The performance of the proposed method is compared with the conventional DASH services. The results proved that the proposed method performs better in terms of reduced error and improved throughput.
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Zhang, Mengmeng, Hongyun Lu et Huihui Bai. « Interlayer Simplified Depth Coding for Quality Scalability on 3D High Efficiency Video Coding ». Scientific World Journal 2014 (2014) : 1–5. http://dx.doi.org/10.1155/2014/841608.

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A quality scalable extension design is proposed for the upcoming 3D video on the emerging standard for High Efficiency Video Coding (HEVC). A novel interlayer simplified depth coding (SDC) prediction tool is added to reduce the amount of bits for depth maps representation by exploiting the correlation between coding layers. To further improve the coding performance, the coded prediction quadtree and texture data from corresponding SDC-coded blocks in the base layer can be used in interlayer simplified depth coding. In the proposed design, the multiloop decoder solution is also extended into the proposed scalable scenario for texture views and depth maps, and will be achieved by the interlayer texture prediction method. The experimental results indicate that the average Bjøntegaard Delta bitrate decrease of 54.4% can be gained in interlayer simplified depth coding prediction tool on multiloop decoder solution compared with simulcast. Consequently, significant rate savings confirm that the proposed method achieves better performance.
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36

Pilka, Filip, et Miloš Oravec. « Prediction Methods for MPEG-4 and H.264 Video Transmission ». Journal of Electrical Engineering 62, no 2 (1 mars 2011) : 57–64. http://dx.doi.org/10.2478/v10187-011-0010-6.

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Prediction Methods for MPEG-4 and H.264 Video Transmission Video services became a large part of internet network traffic. Therefore understanding of video coding standards and video traffic sources, such as video trace files is highly important. In this paper we concentrate on the basic characteristics of mpeg-4 and h.264 video coding standards. We describe the concept of the i, p and b frames in these standards since they are the main feature of every video trace file. Then we describe the content of the video trace files since the trace files are important for researchers to investigate network performance and understanding of network features. These are the important issues in terms of assuring the quality of service (QoS) in multimedia applications spread across the internet. Traffic prediction and bandwidth allocation are the crucial parts in terms of QoS. In this kind of applications, the artificial neural networks are vastly used. Therefore we illustrate the results of neural networks for video traffic prediction using both mpeg-4 and h.264 trace files.
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37

Jabbar, Saba Qasim, et Dheyaa Jasim Kadhim. « A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming ». Journal of Engineering 27, no 1 (1 janvier 2021) : 112–29. http://dx.doi.org/10.31026/10.31026/j.eng.2021.01.08.

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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video streaming, it may also cause a video bitrate oscillation. So the video buffer structure is adjusted by adding two thresholds as operating points for overflow and underflow states to filter the impact of throughput fluctuation on video buffer occupancy level. Then a bandwidth prediction algorithm is proposed for enhancing the performance of video bitrate adaptation. This algorithm's work depends on the current video buffer level, video bitrate of the previous segment, and iterative throughput measurements to predict the best video bitrate for the next segment. Simulation results show that reserving a bandwidth margin is better in adapting the video bitrate under bandwidth variation and then reducing the risk of video playback freezing. Simulation results proved that the playback freezing happens two times: firstly, when there is no bandwidth margin used and secondly, when the bandwidth margin is high while smooth video bitrate is obtained with moderate value. The proposed scheme is compared with other two schemes such as smoothed throughput rate (STR) and Buffer Based Rate (BBR) in terms of prediction error, QoE preferences, buffer size, and startup delay time, then the proposed scheme outperforms these schemes in attaining smooth video bitrates and continuous video playback.
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38

Jabbar, Saba Qasim, et Dheyaa Jasim Kadhim. « A Proposed Adaptive Bitrate Scheme Based on Bandwidth Prediction Algorithm for Smoothly Video Streaming ». Journal of Engineering 27, no 1 (1 janvier 2021) : 112–29. http://dx.doi.org/10.31026/j.eng.2021.01.08.

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A robust video-bitrate adaptive scheme at client-aspect plays a significant role in keeping a good quality of video streaming technology experience. Video quality affects the amount of time the video has turned off playing due to the unfilled buffer state. Therefore to maintain a video streaming continuously with smooth bandwidth fluctuation, a video buffer structure based on adapting the video bitrate is considered in this work. Initially, the video buffer structure is formulated as an optimal control-theoretic problem that combines both video bitrate and video buffer feedback signals. While protecting the video buffer occupancy from exceeding the limited operating level can provide continuous video streaming, it may also cause a video bitrate oscillation. So the video buffer structure is adjusted by adding two thresholds as operating points for overflow and underflow states to filter the impact of throughput fluctuation on video buffer occupancy level. Then a bandwidth prediction algorithm is proposed for enhancing the performance of video bitrate adaptation. This algorithm's work depends on the current video buffer level, video bitrate of the previous segment, and iterative throughput measurements to predict the best video bitrate for the next segment. Simulation results show that reserving a bandwidth margin is better in adapting the video bitrate under bandwidth variation and then reducing the risk of video playback freezing. Simulation results proved that the playback freezing happens two times: firstly, when there is no bandwidth margin used and secondly, when the bandwidth margin is high while smooth video bitrate is obtained with moderate value. The proposed scheme is compared with other two schemes such as smoothed throughput rate (STR) and Buffer Based Rate (BBR) in terms of prediction error, QoE preferences, buffer size, and startup delay time, then the proposed scheme outperforms these schemes in attaining smooth video bitrates and continuous video playback.
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39

Morais, Dario D. R., Lucas S. Althoff, Ravi Prakash, Marcelo M. Carvalho et Mylène C. Q. Farias. « A Content-Based Viewport Prediction Model ». Electronic Imaging 2021, no 9 (18 janvier 2021) : 255–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-255.

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Viewport prediction technologies are often used by most popular adaptive 360-degree video streaming solutions. These solutions stream only the content considered as being more likely to be watched by the final user, with the goal of reducing the volume of network traffic without compromising the user’s Quality of Experience (QoE). In this paper, we propose the Most Viewed Cluster algorithm (MVC), which is a hybrid viewport prediction method. It estimates the user viewport using two types of information: (i) the path of moving objects in the scene and (ii) the viewing behavior of previous users. Preliminary results show that MVC yields good results for long-term predictions.
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40

Bachu, Srinivas, et N. Ramya Teja. « Fuzzy Holoentropy-Based Adaptive Inter-Prediction Mode Selection for H.264 Video Coding ». International Journal of Mobile Computing and Multimedia Communications 10, no 2 (avril 2019) : 42–60. http://dx.doi.org/10.4018/ijmcmc.2019040103.

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Due to the advancement of multimedia and its requirement of communication over the network, video compression has received much attention among the researchers. One of the popular video codings is scalable video coding, referred to as H.264/AVC standard. The major drawback in the H.264 is that it performs the exhaustive search over the interlayer prediction to gain the best rate-distortion performance. To reduce the computation overhead due to exhaustive search on mode prediction process, this paper presents a new technique for inter prediction mode selection based on the fuzzy holoentropy. This proposed scheme utilizes the pixel values and probabilistic distribution of pixel symbols to decide the mode. The adaptive mode selection is introduced here by analyzing the pixel values of the current block to be coded with those of a motion compensated reference block using fuzzy holoentropy. The adaptively selected mode decision can reduce the computation time without affecting the visual quality of frames. Experimentation of the proposed scheme is evaluated by utilizing five videos, and from the analysis, it is evident that proposed scheme has overall high performance with values of 41.367 dB and 0.992 for PSNR and SSIM respectively.
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41

Kulupana, Gosala, Dumidu S. Talagala, Hemantha Kodikara Arachchi et Anil Fernando. « End User Video Quality Prediction and Coding Parameters Selection at the Encoder for Robust HEVC Video Transmission ». IEEE Transactions on Circuits and Systems for Video Technology 29, no 11 (novembre 2019) : 3367–81. http://dx.doi.org/10.1109/tcsvt.2018.2879956.

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42

Lee, Yoon-Soo. « Video Quality Improvement Method of Up-sampling Video by Relationship of Intra Prediction Data and DCT Coefficient ». Journal of the Korea Society of Computer and Information 16, no 7 (31 juillet 2011) : 59–65. http://dx.doi.org/10.9708/jksci.2011.16.7.059.

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43

Huang, Yuanyuan, Dayong Wang et Jianping Li. « Fast inter prediction algorithm in enhancement layer of quality scalable video coding ». JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 25, no 2 (14 mars 2011) : 153–58. http://dx.doi.org/10.3724/sp.j.1187.2011.00153.

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44

Yeh, Chih-Hsuan, Jie-Ru Lin, Mei-Juan Chen, Chia-Hung Yeh, Cheng-An Lee et Kuang-Han Tai. « Fast prediction for quality scalability of High Efficiency Video Coding Scalable Extension ». Journal of Visual Communication and Image Representation 58 (janvier 2019) : 462–76. http://dx.doi.org/10.1016/j.jvcir.2018.12.021.

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45

Wang, Dayong, Ce Zhu, Yu Sun, Frederic Dufaux et Yuanyuan Huang. « Efficient Multi-Strategy Intra Prediction for Quality Scalable High Efficiency Video Coding ». IEEE Transactions on Image Processing 28, no 4 (avril 2019) : 2063–74. http://dx.doi.org/10.1109/tip.2017.2740161.

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46

Blasi, Saverio G., Marta Mrak et Ebroul Izquierdo. « Frequency-Domain Intra Prediction Analysis and Processing for High-Quality Video Coding ». IEEE Transactions on Circuits and Systems for Video Technology 25, no 5 (mai 2015) : 798–811. http://dx.doi.org/10.1109/tcsvt.2014.2359097.

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47

Khaustova, Darya, Jerome Fournier et Olivier Le Meur. « An Objective Metric for Stereoscopic 3D Video Quality Prediction Using Perceptual Thresholds ». SMPTE Motion Imaging Journal 124, no 2 (mars 2015) : 47–55. http://dx.doi.org/10.5594/j18516.

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48

Zhang, Huaizheng, Linsen Dong, Guanyu Gao, Han Hu, Yonggang Wen et Kyle Guan. « DeepQoE : A Multimodal Learning Framework for Video Quality of Experience (QoE) Prediction ». IEEE Transactions on Multimedia 22, no 12 (décembre 2020) : 3210–23. http://dx.doi.org/10.1109/tmm.2020.2973828.

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49

Biernacki, Arkadiusz. « Improving quality of adaptive video by traffic prediction with (F)ARIMA models ». Journal of Communications and Networks 19, no 5 (octobre 2017) : 521–30. http://dx.doi.org/10.1109/jcn.2017.000083.

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50

Tiotsop, Lohic Fotio, Antonio Servetti et Enrico Masala. « Investigating Prediction Accuracy of Full Reference Objective Video Quality Measures through the ITS4S Dataset ». Electronic Imaging 2020, no 11 (26 janvier 2020) : 93–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.11.hvei-093.

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Large subjectively annotated datasets are crucial to the development and testing of objective video quality measures (VQMs). In this work we focus on the recently released ITS4S dataset. Relying on statistical tools, we show that the content of the dataset is rather heterogeneous from the point of view of quality assessment. Such diversity naturally makes the dataset a worthy asset to validate the accuracy of video quality metrics (VQMs). In particular we study the ability of VQMs to model the reduction or the increase of the visibility of distortion due to the spatial activity in the content. The study reveals that VQMs are likely to overestimate the perceived quality of processed video sequences whose source is characterized by few spatial details. We then propose an approach aiming at modeling the impact of spatial activity on distortion visibility when objectively assessing the visual quality of a content. The effectiveness of the proposal is validated on the ITS4S dataset as well as on the Netflix public dataset.
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