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.
Siri et al. (Mon,) studied this question.