基于混合学习和基于hevc的光场编码

Milan Stepanov, G. Valenzise, F. Dufaux
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引用次数: 0

摘要

与传统的图像和视频信号相比,光场具有额外的存储要求,因此需要有效的表示。为了提高编码效率,本文提出了一种将基于学习的压缩方法与传统视频编码方法相结合的混合编码方案。由于基于学习的方法的有效表示,它们的集成在低/中比特率下提供了巨大的收益,并且由于剩余信号的编码,与标准工具相比,在高比特率下具有竞争力。与基于HEVC和JPEG Pleno变换的编解码器相比,该方法分别平均节省38%和31%的BD速率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Learning-Based And Hevc-Based Coding Of Light Fields
Light fields have additional storage requirements compared to conventional image and video signals, and demand therefore an efficient representation. In order to improve coding efficiency, in this work we propose a hybrid coding scheme which combines a learning-based compression approach with a traditional video coding scheme. Their integration offers great gains at low/mid bitrates thanks to the efficient representation of the learning-based approach and is competitive at high bitrates compared to standard tools thanks to the encoding of the residual signal. The proposed approach achieves on average 38% and 31% BD rate saving compared to HEVC and JPEG Pleno transform-based codec, respectively.
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