HyPIC: Rethinking Residual Representation for Progressive Image Compression

Progressive image compression enables bandwidth-adaptive transmission by successively refining a coarse representation. These refinements can be encoded as residuals, allowing each stage to describe information not captured by the preceding approximation. Residual vector quantization (RVQ) implements this idea in learned codecs by quantizing the remaining latent residual and accumulating codewords to approximate the encoder output. We challenge latent residual approximation as the organizing principle of progressive refinement. Once a prefix establishes a base reconstruction, the remaining information should be encoded in a representation learned for improving the decoded image, without requiring faithful recovery of the original latent residual. To this end, we introduce Subspace-Projected Refinement(SPR), which jointly learns a compact representation of residual information and the directions through which its decoded coefficients update the VQ prefix. SPR is optimized for image rate–distortion, with a coding dimensionality chosen independently of the base latent's channel dimension. Individual coefficients adjust learned correction directions, while their synthesis jointly modifies the original latent channels. This structure leads to Hybrid Progressive Image Compression (HyPIC): VQ establishes the base reconstruction through shared joint patterns, and scalar quantization (SQ) encodes image-specific corrections through the learned SPR coefficients. Experimental results show that HyPIC attains competitive compression performance compared with previous methods. It achieves LPIPS BD-rate savings of up to 75.46% over Control-GIC across the four evaluated datasets. Beyond perceptual quality, HyPIC enables progressive transmission for flexibility, and also delivers faster encoding and decoding compared with ProGIC on GPUs for efficiency.