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Recently many works attempt to develop image compression models based on deeplearning architectures, where the uniform scalar quantizer (SQ) is commonlyapplied to the feature maps between the encoder and decoder. In this paper, wepropose to incorporate trellis coded quantizer (TCQ) into a deep learning basedimage compression framework. A soft-to-hard strategy is applied to allow forback propagation during training. We develop a simple image compression modelthat consists of three subnetworks (encoder, decoder and entropy estimation),and optimize all of the components in an end-to-end manner. We experiment ontwo high resolution image datasets and both show that our model can achievesuperior performance at low bit rates. We also show the comparisons between TCQand SQ based on our proposed baseline model and demonstrate the advantage ofTCQ.

Deep Learning-based Image Compression with Trellis Coded Quantization