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Recent advancements in deep learning-based image compression are notable.However, prevalent schemes that employ a serial context-adaptive entropy modelto enhance rate-distortion (R-D) performance are markedly slow. Furthermore,the complexities of the encoding and decoding networks are substantially high,rendering them unsuitable for some practical applications. In this paper, wepropose two techniques to balance the trade-off between complexity andperformance. First, we introduce two branching coding networks to independentlylearn a low-resolution latent representation and a high-resolution latentrepresentation of the input image, discriminatively representing the global andlocal information therein. Second, we utilize the high-resolution latentrepresentation as conditional information for the low-resolution latentrepresentation, furnishing it with global information, thus aiding in thereduction of redundancy between low-resolution information. We do not utilizeany serial entropy models. Instead, we employ a parallel channel-wiseauto-regressive entropy model for encoding and decoding low-resolution andhigh-resolution latent representations. Experiments demonstrate that our methodis approximately twice as fast in both encoding and decoding compared to theparallelizable checkerboard context model, and it also achieves a 1.2%improvement in R-D performance compared to state-of-the-art learned imagecompression schemes. Our method also outperforms classical image codecsincluding H.266/VVC-intra (4:4:4) and some recent learned methods inrate-distortion performance, as validated by both PSNR and MS-SSIM metrics onthe Kodak dataset.

Learned Image Compression with Dual-Branch Encoder and Conditional Information Coding