标准HEVC码流的多尺度深度解码器

Tingting Wang, Wenhui Xiao, Mingjin Chen, Hongyang Chao
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引用次数: 12

摘要

众所周知,视频帧之间具有很强的多尺度相似性。然而,目前的视频编码标准几乎没有考虑到这种相似性。在编码器端利用多尺度信息存在两个主要问题:一是新的运动参数带来的额外的运动模型和开销;二是编码算法复杂度的急剧增加。是否可以仅在解码器端使用多尺度相似度来提高解码后视频的质量,即进一步提高编码效率?本文主要研究如何解决这个问题,提出了一种新的HEVC多尺度深度解码器(MSDD)。得益于深度学习技术(卷积神经网络和长短期记忆网络)的效率,MSDD在不改变任何编码算法的情况下,仅在解码器端实现更高的编码效率。大量的实验验证了MSDD的可行性和有效性。对于AI、LP、LB和RA编码配置,MSDD与HEVC锚相比,平均降低了6.5%、8.0%、6.4%和6.7%的bd速率。特别是对于具有多尺度相似度的视频,该方法确实明显提高了编码效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Multi-Scale Deep Decoder for the Standard HEVC Bitstreams
As we all know, there is strong multi-scale similarity among video frames. However, almost none of the current video coding standards takes this similarity into consideration. There exist two major problems when utilizing the multi-scale information at encoder-end: one is the extra motion models and the overheads brought by new motion parameters; the other is the extreme increment of the encoding algorithms’ complexity. Is it possible to employ the multi-scale similarity only at the decoder-end to improve the decoded videos’ quality, i.e., to further boost the coding efficiency? This paper mainly studies how to answer this question by proposing a novel Multi-Scale Deep Decoder (MSDD) for HEVC. Benefiting from the efficiency of deep learning technology (Convolutional Neural Network and Long Short-Term Memory network), MSDD achieves a higher coding efficiency only at the decoder-end without changing any encoding algorithms. Extensive experiments validate the feasibility and effectiveness of MSDD. MSDD leads to on averagely 6.5%, 8.0%, 6.4%, and 6.7% BD-rate reduction compared to HEVC anchor, for AI, LP, LB and RA coding configurations respectively. Especially for the videos with multi-scale similarity, the proposed approach obviously improves the coding efficiency indeed.
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