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While convolution and self-attention are extensively used in learned imagecompression (LIC) for transform coding, this paper proposes an alternativecalled Contextual Clustering based LIC (CLIC) which primarily relies onclustering operations and local attention for correlation characterization andcompact representation of an image. As seen, CLIC expands the receptive fieldinto the entire image for intra-cluster feature aggregation. Afterward,features are reordered to their original spatial positions to pass through thelocal attention units for inter-cluster embedding. Additionally, we introducethe Guided Post-Quantization Filtering (GuidedPQF) into CLIC, effectivelymitigating the propagation and accumulation of quantization errors at theinitial decoding stage. Extensive experiments demonstrate the superiorperformance of CLIC over state-of-the-art works: when optimized using MSE, itoutperforms VVC by about 10% BD-Rate in three widely-used benchmark datasets;when optimized using MS-SSIM, it saves more than 50% BD-Rate over VVC. Our CLICoffers a new way to generate compact representations for image compression,which also provides a novel direction along the line of LIC development.

Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding