Hybrid-context-based multi-prior entropy modeling for learned lossless image compression

Lossless image compression is an essential aspect of image processing, particularly in many fields that require high information fidelity. In recent years, learned lossless image compression methods have shown promising results. However, many of these methods do not make optimal use of available information, leading to suboptimal performance. This paper proposes a multi -prior entropy model for lossless image compression, which effectively leverages available information to achieve better compression performance. The proposed multiprior comprises a cross -channel prior, hybrid local context, and hyperprior, allowing it to effectively utilize all available information. To remove redundancy across color channels, the original image is first losslessly transformed into YUV color space. The network then learns priors from the original image, the prior -coding channels, and the local context, which are fused to form the multi -prior used for GMM parameters estimation. Moreover, to capture the features of different images, a hybrid local context is abstracted using different kernel sizes of mask convolutions in a local context. The experimental results on several datasets demonstrate that our algorithm outperforms several existing learning -based image compression methods and traditional methods, such as JPEG2000, WebP, and FLIF.