Low-Complexity Error Resilient HEVC Video Coding: A Deep Learning Approach

Wang, Taiyu; Li, Fan; Qiao, Xiaoya; Cosman, Pamela C.

doi:10.1109/tip.2020.3043124

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…A low-complexity in-loop filter model is presented for mobile multimedia [11]. A neural network-based inter-prediction scheme is introduced for video compression [12], and deep learning is employed for low-complexity error resilient video coding [13]. ML-based solutions are proposed for HTTP adaptive streaming [14], and a reinforcement learning framework is introduced for frame-level bit allocation in HEVC/H.265 [15].…”

Section: ░ 2 Related Researchmentioning

confidence: 99%

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Charles,

Khan,

Rao

2023

IJEER

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Predicting future frames and improving inter-frame prediction are ongoing challenges in the field of video streaming. By creating a novel framework called STreamNet (Spatial-Temporal Video Coding), fusing bidirectional long short-term memory with temporal convolutional networks, this work aims to address the issue at hand. The development of STreamNet, which combines spatial hierarchies with local and global temporal dependencies in a seamless manner, along with sophisticated preprocessing, attention mechanisms, residual learning, and effective compression techniques, is the main contribution. Significantly, STreamNet claims to provide improved video coding quality and efficiency, making it suitable for next-generation networks. STreamNet has the potential to provide reliable and optimal streaming in high-demand network environments, as shown by preliminary tests that show a performance advantage over existing methods.

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Section: ░ 2 Related Researchmentioning

confidence: 99%

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Charles,

Khan,

Rao

2023

IJEER

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“…This strategy helps to confine the error propagation to smaller areas. This is considered in [ 87 ], where a deep learning framework is presented for deciding the optimal intra and inter CU partition. The prediction is based on deep CNN based on multi-scale information fusions for better error resilience.…”

Section: Learning-based Transmissionmentioning

confidence: 99%

Machine Learning for Multimedia Communications

Thomos

Maugey

Toni

2022

Sensors

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Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

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“…There are universal methods like Lempel-Ziv-Welch [12,13] that code any kind of data, although they do not achieve the high compression ratios that specifically devised systems yield. In recent years, deep-learning techniques have been spread in many compression schemes to enhance transformation and prediction techniques, obtaining competitive results in many fields [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Fast and Efficient Entropy Coding Architectures for Massive Data Compression

Auli-Llinas

2023

Technologies

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The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems.

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Low-Complexity Error Resilient HEVC Video Coding: A Deep Learning Approach

Cited by 11 publications

References 38 publications

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Machine Learning for Multimedia Communications

Fast and Efficient Entropy Coding Architectures for Massive Data Compression

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