Abstract
While deep learning-based image compression methods have shown impressive coding performance, most existing methods are still in the mire of two limitations: (1) unpredictable compression efficiency gain when adopting convolutional neural networks with different depths, and (2) lack of an accurate model to estimate the entropy during the training process. To address these two problems, in this paper, a deep multi-stage representation based image compression (MSRIC) method is proposed. Owing to this architecture, the detail information of shallow stages and the compact information of deep stages can be utilized for image reconstruction. Furthermore, a data-dependent channel-wised factorized probability model (DCFPM) is adopted to increase the accuracy of entropy estimation. Experimental results indicate that the proposed method guarantees better perceptual performance at a wide range of bit-rates. Also, ablation studies are carried out to validate the above mentioned technologies.
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