  While convolution and self-attention are extensively used in learned image
compression (LIC) for transform coding, this paper proposes an alternative
called Contextual Clustering based LIC (CLIC) which primarily relies on
clustering operations and local attention for correlation characterization and
compact representation of an image. As seen, CLIC expands the receptive field
into the entire image for intra-cluster feature aggregation. Afterward,
features are reordered to their original spatial positions to pass through the
local attention units for inter-cluster embedding. Additionally, we introduce
the Guided Post-Quantization Filtering (GuidedPQF) into CLIC, effectively
mitigating the propagation and accumulation of quantization errors at the
initial decoding stage. Extensive experiments demonstrate the superior
performance of CLIC over state-of-the-art works: when optimized using MSE, it
outperforms 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 CLIC
offers a new way to generate compact representations for image compression,
which also provides a novel direction along the line of LIC development.
