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.

翻译：近年来，学习图像压缩方法已取得显著的性能提升。然而，获取更紧凑的潜在表示并减少量化误差的影响仍是该领域的关键挑战。为应对这些挑战，我们提出了特征提取模块、特征细化模块和特征增强模块。我们的特征提取模块对图像中的像素进行重排，将生成图像分割为子图像，并从子图像中提取粗粒度特征。特征细化模块将粗粒度特征堆叠，并采用由级联三维卷积残差块构成的注意力细化块，通过利用跨通道、子图像内以及子图像间的相关性来学习更紧凑的潜在特征。特征增强模块则减少量化后解码特征中的信息损失。我们还提出了量化误差补偿模块，以缓解训练与测试间的量化失配问题。所提出的四个模块可便捷地集成到先进的学习图像压缩方法中。实验表明，在Kodak数据集和CLIC数据集上，将我们的模块与Tiny-LIC结合使用时，在峰值信噪比和多尺度结构相似性指标上均优于现有学习图像压缩方法及图像压缩标准。