Neural image compression methods have seen increasingly strong performance in recent years. However, they suffer orders of magnitude higher computational complexity compared to traditional codecs, which hinders their real-world deployment. This paper takes a step forward towards closing this gap in decoding complexity by using a shallow or even linear decoding transform resembling that of JPEG. To compensate for the resulting drop in compression performance, we exploit the often asymmetrical computation budget between encoding and decoding, by adopting more powerful encoder networks and iterative encoding. We theoretically formalize the intuition behind, and our experimental results establish a new frontier in the trade-off between rate-distortion and decoding complexity for neural image compression. Specifically, we achieve rate-distortion performance competitive with the established mean-scale hyperprior architecture of Minnen et al. (2018) at less than 50K decoding FLOPs/pixel, reducing the baseline's overall decoding complexity by 80%, or over 90% for the synthesis transform alone. Our code can be found at https://github.com/mandt-lab/shallow-ntc.

翻译：神经图像压缩方法近年来性能显著提升，但相比传统编解码器，其计算复杂度高出数个数量级，阻碍了实际部署。本文通过采用类似JPEG的浅层甚至线性解码变换，向弥合这一解码复杂度差距迈出关键一步。为补偿由此带来的压缩性能衰减，我们利用编码与解码之间通常不对称的计算预算，采用更强大的编码器网络和迭代编码方式。我们对这一直觉进行了理论形式化，实验结果表明，本文在神经图像压缩的率失真与解码复杂度权衡方面建立了新基准。具体而言，我们在解码FLOPs低于50K/像素的条件下实现了与Minnen等人（2018）经典均值尺度超先验架构相当的率失真性能，将基线模型的整体解码复杂度降低80%，其中合成变换部分的复杂度降低超过90%。代码已开源至https://github.com/mandt-lab/shallow-ntc。