Learned Image Compression via Unevenly Grouped Channel Context Gaussian Mixture Entropy Modeling
While deep learning-based image compression has achieved remarkable rate-distortion performance, existing entropy models are often hindered by rigid channel-wise grouping and oversimplified probability distribution assumptions. To address these limitations, this paper proposes a novel entropy modeling framework integrating an Unevenly Grouped Channel Context Model (UGCCM) and a Gaussian Mixture Model (GMM). Specifically, leveraging the information compaction characteristics of latent representations, we design a progressive channel grouping strategy that assigns finer granularity to information-rich initial channels, thereby maximizing contextual information utilization. Furthermore, we replace the conventional single Gaussian prior with a multi-component Gaussian mixture model, significantly enhancing the flexibility and accuracy of latent distribution estimations. Experimental results demonstrate that our framework achieves consistent rate-distortion improvements, yielding over a 0.15 dB PSNR gain at equivalent bitrates on standard test sets compared to baseline models. Moreover, subjective quality assessments confirm our method’s superior capability in preserving complex textures and fine visual details.