Computationally Efficient Neural Image Compression

Image compression using neural networks have reached or exceeded non-neural methods (such as JPEG, WebP, BPG). While these networks are state of the art in rate-distortion performance, computational feasibility of these models remains a challenge. Our work provides three novel contributions. We propose a run-time improvement to the Generalized Divisive Normalization formulation, a regularization technique targeted to optimizing neural image decoders, and an analysis of the trade offs in 207 architecture variations across multiple distortion loss functions to recommend an architecture that is twice as fast while maintaining state-of-the-art image compression performance.