论文标题
基于学习的有条件图像编码器使用颜色分离
Learning-Based Conditional Image Coder Using Color Separation
论文作者
论文摘要
最近，基于神经网络（NN）的图像压缩编解码器优于最先进的经典bpg，例如BPG，BPG是基于HEVC Intra的图像格式。但是，典型的NN编解码器具有很高的复杂性，并且对并行数据处理的选项有限。在这项工作中，我们提出了一个条件分离原则，旨在改善并行化并降低NN编解码器的计算要求。我们提出了遵循此原理的条件颜色分离（CCS）编解码器。图像的颜色成分分为主要和非主要成分。通过共同训练的网络分别进行每个组件的处理。我们的方法允许平行处理每个组件，灵活性选择不同的通道编号以及总体复杂性降低。 CCS编解码器的记忆使用少40％以上，编码速度更快2倍，而解码速度更快22％，与BPG相比，RGB PSNR的BD-PSNR中只有4％的BD率损失。
Recently, image compression codecs based on Neural Networks(NN) outperformed the state-of-art classic ones such as BPG, an image format based on HEVC intra. However, the typical NN codec has high complexity, and it has limited options for parallel data processing. In this work, we propose a conditional separation principle that aims to improve parallelization and lower the computational requirements of an NN codec. We present a Conditional Color Separation (CCS) codec which follows this principle. The color components of an image are split into primary and non-primary ones. The processing of each component is done separately, by jointly trained networks. Our approach allows parallel processing of each component, flexibility to select different channel numbers, and an overall complexity reduction. The CCS codec uses over 40% less memory, has 2x faster encoding and 22% faster decoding speed, with only 4% BD-rate loss in RGB PSNR compared to our baseline model over BPG.
