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CNN-Based Fast HEVC Quantization Parameter Mode Decision

Liming Chen1, Bosi Wang1,*, Weijie Yu1, Xu Fan1

School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.

*Corresponding Author: Bosi Wang. Email: email.

Journal of New Media 2019, 1(3), 115-126. https://doi.org/10.32604/jnm.2019.08581

Abstract

With the development of multimedia presentation technology, image acquisition technology and the Internet industry, long-distance communication methods have changed from the previous letter, the audio to the current audio/video. And the proportion of video in work, study and entertainment keeps increasing, high-definition video is getting more and more attention. Due to the limits of the network environment and storage capacity, the original video must be encoded to be efficiently transmitted and stored. High Efficient Video Coding (HEVC) requires a large amount of time to recursively traverse all possible quantization parameter values of the coding unit in the adaptive quantization process. The optimal quantization parameter is calculated by comparing the rate distortion cost. In this paper, we propose a fast decision method for HEVC quantization parameters selection based on convolutional neural network, which saves video’s encoding time.

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APA Style
Chen, L., Wang, B., Yu, W., Fan, X. (2019). Cnn-based fast HEVC quantization parameter mode decision. Journal of New Media, 1(3), 115-126. https://doi.org/10.32604/jnm.2019.08581
Vancouver Style
Chen L, Wang B, Yu W, Fan X. Cnn-based fast HEVC quantization parameter mode decision. J New Media . 2019;1(3):115-126 https://doi.org/10.32604/jnm.2019.08581
IEEE Style
L. Chen, B. Wang, W. Yu, and X. Fan "CNN-Based Fast HEVC Quantization Parameter Mode Decision," J. New Media , vol. 1, no. 3, pp. 115-126. 2019. https://doi.org/10.32604/jnm.2019.08581



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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