GLIC: General Format Learned Image Compression

Abstract

Learned image lossy compression techniques have surpassed traditional methods in both subjective vision and quantitative evaluation. However, current models are only applicable to three-channel image formats, limiting their practical application due to the diversity and complexity of image formats. We propose a high-performance learned image compression model for general image formats. We first introduce a transfer method to unify any-channel image formats, enhancing the applicability of neural networks. This method's effectiveness is demonstrated through image information entropy and image homomorphism theory. Then, we introduce an adaptive attention residual block into the entropy model to give it better generalization ability. Meanwhile, we propose an evenly grouped cross-channel context module for progressive preview image decoding. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) in the field of learned image compression in terms of PSNR and MS-SSIM. This work extends the applicability of learned image compression techniques to more practical production environments.