INTEGER QUANTIZED LEARNED IMAGE COMPRESSION

Learned image compression (LIC) has shown remarkable improvement compared to traditional methods, but requires increased computational and memory complexity. Network quantization is an effective way to resolve the issue of complexity, but quantization of LIC has not been explored much. In this paper, we propose an integer quantized LIC (IQ-LIC) via static quantization of both weights and activations as integers. We design a quantized convolution layer involving a new Leaky-Clip module. We also propose a squared quantization error loss to help efficient quantization-aware training. Experimental results show that IQ-LIC achieves better rate-distortion performance compared to existing methods.