Inter-Image Predictive Compression of JPEG Collections Using DCT-Domain Graph-Based Residual Coding
Year: 2026
Type: article
Abstract: Social media platforms, surveillance systems, and medical imaging systems produce large image collections that require significant storage space. Traditional JPEG compression processes each image independently, preventing the exploitation of redundancy among similar images. This paper proposes a lossless inter-image predictive compression framework operating in the Discrete Cosine Transform (DCT) domain to improve compression efficiency for JPEG collections. A graph-based prediction structure is constructed using similarity features between images. Residual modeling is then applied relative to selected reference images, followed by shared entropy coding across the collection. Experimental results demonstrate that the proposed framework achieves 86.62% compression efficiency compared with 78.76% efficiency obtained from independent JPEG compression, providing approximately 8% additional bit savings while maintaining lossless reconstruction.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7140076325
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