ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization

• URL: http://arxiv.org/abs/2603.02897v1
• Date: Tue, 03 Mar 2026 11:47:05 GMT
• Title: ProGIC: Progressive and Lightweight Generative Image Compression with Residual Vector Quantization
• Authors: Hao Cao, Chengbin Liang, Wenqi Guo, Zhijin Qin, Jungong Han,
• Abstract summary: We propose Progressive Generative Image Compression (ProGIC), a compact built on residual vector quantization (RVQ)<n>In RVQ, a sequence of vector quantizers encodes the residuals stage by stage, each with its own codebook.<n>We pair this with a lightweight backbone based on depthwise-separable convolutions and small attention blocks, enabling practical deployment on both GPU and CPU-only devices.
• Score: 59.481950697968706
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in generative image compression (GIC) have delivered remarkable improvements in perceptual quality. However, many GICs rely on large-scale and rigid models, which severely constrain their utility for flexible transmission and practical deployment in low-bitrate scenarios. To address these issues, we propose Progressive Generative Image Compression (ProGIC), a compact codec built on residual vector quantization (RVQ). In RVQ, a sequence of vector quantizers encodes the residuals stage by stage, each with its own codebook. The resulting codewords sum to a coarse-to-fine reconstruction and a progressive bitstream, enabling previews from partial data. We pair this with a lightweight backbone based on depthwise-separable convolutions and small attention blocks, enabling practical deployment on both GPUs and CPU-only devices. Experimental results show that ProGIC attains comparable compression performance compared with previous methods. It achieves bitrate savings of up to 57.57% on DISTS and 58.83% on LPIPS compared to MS-ILLM on the Kodak dataset. Beyond perceptual quality, ProGIC enables progressive transmission for flexibility, and also delivers over 10 times faster encoding and decoding compared with MS-ILLM on GPUs for efficiency.

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