A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natural images

Alam, Mushfiqul; Nguyen, Tan Van; Hagan, Martin T.; Chandler, Damon M.

doi:10.1117/12.2188913

Cited by 20 publications

(13 citation statements)

References 33 publications

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“…Recently, a three-layer DNN was developed to predict the local visibility threshold CT for each CTU, by which more accurate quantization could be achieved via the connection between CT and actual quantization step size. This development led to noticeable R-D improvement, for example, up to 11%, as reported in [125].…”

Section: A Modularized Neural Video Codingmentioning

confidence: 69%

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

Ding

Chen

et al. 2021

Proc. IEEE

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Significant advances in video compression systems have been made in the past several decades to satisfy the near-exponential growth of Internet-scale video traffic. From the application perspective, we have identified three major functional blocks, including preprocessing, coding, and postprocessing, which have been continuously investigated to maximize the end-user quality of experience (QoE) under a limited bit rate budget. Recently, artificial intelligence (AI)-powered techniques have shown great potential to further increase the efficiency of the aforementioned functional blocks, both individually and jointly. In this article, we review recent technical advances in video compression systems extensively, with an emphasis on deep neural network (DNN)based approaches, and then present three comprehensive case studies. On preprocessing, we show a switchable texturebased video coding example that leverages DNN-based scene understanding to extract semantic areas for the improvement Manuscript

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Section: A Modularized Neural Video Codingmentioning

confidence: 69%

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

Ding

Chen

et al. 2021

Proc. IEEE

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“…Comparatively, the experiment results can achieve 36.8% complexity reduction on average with only 3.0% bitrate increase. In Alam's work [8], a fast Convolutional-Neural-Network (CNN) based quantization strategy for HEVC was proposed. They utilized the contrast gain control model to develop a structural facilitation model to capture effects of recognizable structures on distortion visibility.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Method for Optimizing Partition of Prediction Block in Coding Unit in H.265/HEVC

Jiang

Yang

Xie

et al. 2020

Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cy

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“…In [3], a fast convolutional-neural-network based quantization strategy for HEVC was proposed. Local artifact visibility is predicted via a network trained on data derived from an improved contrast gain control model.…”

Section: Literature Reviewmentioning

confidence: 99%

Using CNN for Encoder Optimization in H.265/HEVC

Xie

Yang

et al. 2017

Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications

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In this work-in-progress paper, we proposed using deep learning techniques, especially the deep Convolutional Neural Network (CNN) to perform critical tasks of video ending within the framework of H.265/HEVC. Deep CNNs have achieved breakthrough improvements on image recognition tasks such as image classifications, object identifications, and image annotations. However, very few work has been done in applying deep CNN to video encoding. In order to take advantage of the significant capabilities of deep CNN on image content detection, we proposed using deep CNN as the primary technique to perform critical tasks in video encoding that are relevant to the contents of one or multiple video frames. More specifically, we designed deep CNNs for the following tasks in H.265/HEVC encoder: partitioning CTU to CUs; partitioning CU to PUs; performing intra prediction; and performing inter predictions.

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A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natural images

Cited by 20 publications

References 33 publications

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

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

A Machine Learning Method for Optimizing Partition of Prediction Block in Coding Unit in H.265/HEVC

Using CNN for Encoder Optimization in H.265/HEVC

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