Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation

• URL: http://arxiv.org/abs/2502.15188v1
• Date: Fri, 21 Feb 2025 03:40:27 GMT
• Title: Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation
• Authors: Shiqi Jiang, Hui Yuan, Shuai Li, Raouf Hamzaoui, Xu Wang, Junyan Huo,
• Abstract summary: We propose a feature extraction module, a feature refinement module, and a feature enhancement module.<n>Our four modules can be readily integrated into state-of-the-art LIC methods.<n>Experiments show that combining our modules with Tiny-LIC outperforms existing LIC methods and image compression standards in terms of peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) on the Kodak dataset and the CLIC dataset.
• Score: 18.15640294602421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, learned image compression (LIC) methods have achieved significant performance improvements. However, obtaining a more compact latent representation and reducing the impact of quantization errors remain key challenges in the field of LIC. To address these challenges, we propose a feature extraction module, a feature refinement module, and a feature enhancement module. Our feature extraction module shuffles the pixels in the image, splits the resulting image into sub-images, and extracts coarse features from the sub-images. Our feature refinement module stacks the coarse features and uses an attention refinement block composed of concatenated three-dimensional convolution residual blocks to learn more compact latent features by exploiting correlations across channels, within sub-images (intra-sub-image correlations), and across sub-images (inter-sub-image correlations). Our feature enhancement module reduces information loss in the decoded features following quantization. We also propose a quantization error compensation module that mitigates the quantization mismatch between training and testing. Our four modules can be readily integrated into state-of-the-art LIC methods. Experiments show that combining our modules with Tiny-LIC outperforms existing LIC methods and image compression standards in terms of peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) on the Kodak dataset and the CLIC dataset.

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