Comprehensive Comparisons Of Uniform Quantizers For Deep Image Compression

Deep image compression is formulated as a joint rate-distortion optimization problem using an auto-encoder architecture. Latent representations obtained by an encoder are quantized by a quantizer and fed to a decoder and an entropy model to reconstruct the images and estimate probabilities for entropy coding, respectively. Existing methods presented several methods to approximate the quantization for optimization because the gradient of a naive quantizer is zero almost everywhere. Although quantization is a fundamental operation in image compression, there are few comparisons between these quantization methods and the best approximation among them remains unexplored. To address this problem, we comprehensively compare existing approximations of the uniform quantization. Furthermore, focusing on the fact that a decoder and an entropy model have different compatibility with the approximation of quantization, we also evaluate different combinations of approximations for the decoder and the entropy model. Through experiments, we find that the approximations by adding noise are better than rounding and that the best combination of approximations among what we explored outperforms existing approximations.