Learning-based imagecoding has attracted increasing attentions for its higher compression efficiency than reigning image codecs. However, most existing learning-based codecs do not support variable rates with a singl...
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ISBN:
(纸本)9798400701085
Learning-based imagecoding has attracted increasing attentions for its higher compression efficiency than reigning image codecs. However, most existing learning-based codecs do not support variable rates with a single encoder;their decoders are also of fixed, high computational complexity. In this paper, we propose an End-to-end, Learning-based and Flexible image Codec (ELFIC) that supports variable rate and flexible decoding complexity. First, we propose a general image codec with Nonlinear Feature Fusion Transform (NFFT) as nonlinear transforms to improve its Rate-Distortion (RD) performance. Second, we propose an Instance-aware Decoding Complexity Allocation (IDCA) approach, which exploits image contents for a tradeoff between reconstruction quality and computational complexity in the decoding process. Third, we propose an RD-Complexity (RDC) optimization algorithm, which maximizes the image quality under given rate and complexity constraints for the whole framework. Experimental results show that ELFIC achieves variable rate, flexible decoding complexity with the state-of-the-art RD performance. It also supports a more efficient decoding process by focusing on image contents. Source codes are available at https://***/Zhichen-Zhang/ELFIC-image-Compression.
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