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Learned image compression (LIC) has reached the traditional hand-craftedmethods such as JPEG2000 and BPG in terms of the coding gain. However, thelarge model size of the network prohibits the usage of LIC on resource-limitedembedded systems. This paper presents a LIC with 8-bit fixed-point weights.First, we quantize the weights in groups and propose a non-linear memory-freecodebook. Second, we explore the optimal grouping and quantization scheme.Finally, we develop a novel weight clipping fine tuning scheme. Experimentalresults illustrate that the coding loss caused by the quantization is small,while around 75% model size can be reduced compared with the 32-bitfloating-point anchor. As far as we know, this is the first work to explore andevaluate the LIC fully with fixed-point weights, and our proposed quantized LICis able to outperform BPG in terms of MS-SSIM.

End-to-end Learned Image Compression with Fixed Point Weight Quantization