Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Ding, Dandan; Ma, Zhan; Chen, Di; Chen, Qingshuang; Liu, Zoe; Zhu, Fengqing

doi:10.1109/jproc.2021.3059994

Cited by 46 publications

(21 citation statements)

References 203 publications

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“…Commonly, the objective is to minimize a reconstruction error (Eq. 19), as in the k-means algorithm, but can also more generally be an operational rate-distortion trade-off (Eq. 22).…”

Section: Vector Quantizationmentioning

confidence: 99%

See 1 more Smart Citation

An Introduction to Neural Data Compression

Yang¹,

Mandt²,

Theis³

2022

Preprint

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Neural compression is the application of neural networks and other machine learning methods to data compression. While machine learning deals with many concepts closely related to compression, entering the field of neural compression can be difficult due to its reliance on information theory, perceptual metrics, and other knowledge specific to the field. This introduction hopes to fill in the necessary background by reviewing basic coding topics such as entropy coding and rate-distortion theory, related machine learning ideas such as bits-back coding and perceptual metrics, and providing a guide through the representative works in the literature so far.

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“…Commonly, the objective is to minimize a reconstruction error (Eq. 19), as in the k-means algorithm, but can also more generally be an operational rate-distortion trade-off (Eq. 22).…”

Section: Vector Quantizationmentioning

confidence: 99%

“…not necessarily data compression. We hope to complement existing surveys that have a more applied focus on data compression [17] [18] [19] by highlighting the connections to generative modeling and machine learning in general.…”

Section: Introductionmentioning

confidence: 99%

An Introduction to Neural Data Compression

Yang¹,

Mandt²,

Theis³

2022

Preprint

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“…Some very recent works have however tried to avoid the use of the classical predictive structure involved in a classical video coder [ 57 ]. A detailed review is presented in [ 58 ].…”

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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“…Meanwhile, researchers focusing on content-aware encoding attempt to improve the coding efficiency by removing visual redundancy to further improve the compression ratio. One notable approach is to use saliency detection for region-wise quantization control before the traditional image compression framework [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images

2022

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Due to the limited storage space of spacecraft and downlink bandwidth in the data delivery during planetary exploration, an efficient way for image compression onboard is essential to reduce the volume of acquired data. Applicable for planetary images, this study proposes a perceptual adaptive quantization technique based on Convolutional Neural Network (CNN) and High Efficiency Video Coding (HEVC). This technique is used for bitrate reduction while maintaining the subjective visual quality. The proposed algorithm adaptively determines the Coding Tree Unit (CTU) level Quantization Parameter (QP) values in HEVC intra-coding using the high-level features extracted by CNN. A modified model based on the residual network is exploited to extract the saliency map for a given image automatically. Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation. Several quantitative performance metrics that efficiently correlate with human perception are used for evaluating image quality. The experimental results reveal that the proposed algorithm achieves better visual quality along with a maximum of 7.17% reduction in the bitrate as compared to the standard HEVC coding.

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Advances in Video Compression System Using Deep Neural Network: A Review and Case Studies

Cited by 46 publications

References 203 publications

An Introduction to Neural Data Compression

An Introduction to Neural Data Compression

Machine Learning for Multimedia Communications

Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images

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