Abstract
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance. Most existing methods adopt spatially invariant bit length allocation and incorporate discrete entropy approximation to constrain compression rate. Nonetheless, the information content is spatially variant, where the regions with complex and salient structures generally are more essential to image compression. Taking the spatial variation of image content into account, this paper presents a content-weighted encoder-decoder model, which involves an importance map subnet to produce the importance mask for locally adaptive bit rate allocation. Consequently, the summation of importance mask can thus be utilized as an alternative of entropy estimation for compression rate control. Furthermore, the quantized representations of the learned code and importance map are still spatially dependent, which can be losslessly compressed using arithmetic coding. To compress the codes effectively and efficiently, we propose a trimmed convolutional network to predict the conditional probability of quantized codes. Experiments show that the proposed method can produce visually much better results, and performs favorably in comparison with deep and traditional lossy image compression approaches.
Abstract (translated)
基于学习的有损图像压缩通常涉及速率失真性能的联合优化。现有的方法大多采用空间不变的位长分配，并结合离散熵近似来约束压缩率。尽管如此，信息内容在空间上是变化的，其中具有复杂和突出结构的区域通常对图像压缩更为重要。考虑到图像内容的空间变化，提出了一种内容加权的编码器-解码器模型，该模型包含一个重要映射子网，用于产生局部自适应比特率分配的重要掩码。因此，重要性掩模的求和可以作为压缩速率控制的熵估计的替代方法。此外，学习码和重要性图的量化表示仍然是空间依赖的，可以使用算术编码进行无损压缩。为了有效地压缩编码，我们提出了一种修剪卷积网络来预测量化编码的条件概率。实验表明，与传统的有损图像压缩方法和深度压缩方法相比，该方法在视觉上能获得较好的效果。
URL
https://arxiv.org/abs/1904.00664
