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Inter-Image Predictive Compression of JPEG Collections Using DCT-Domain Graph-Based Residual Coding
Subject area: Science,Engineering and Technology · Area of research: Digital Image Processing
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.
Keywords
Lossless Image Compression, JPEG Collections, DCT-Domain Coding, Graph-Based Prediction, Residual Coding, Entropy Coding
References
[1] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 4th ed., Pearson Education, 2018.
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[5] I. Matsuda, Y. Nomoto, K. Wakabayashi, and S. Itoh, “Lossless re-encoding of JPEG images using block-adaptive intra prediction,” in Proc. 16th Eur. Signal Process. Conf., Aug. 2008, pp. 1–5.
[6] N. Ponomarenko, K. Egiazarian, V. Lukin, and J. Astola, “Additional lossless compression of JPEG images,” in Proc. 4th Int. Symp. Image Signal Process. Anal. (ISPA), Sep. 2005, pp. 117–120.
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How to cite this paper
@article{1715313,
author = {Elchuri Venkata Siri, Chekuri Hema Sri, Gella Bhanu Sri, Ganganaboina Venkata Gowtham, Smt. I. Naga Padmaja},
title = {Inter-Image Predictive Compression of JPEG Collections Using DCT-Domain Graph-Based Residual Coding},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {1697-1703},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715313.pdf},
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.},
keywords = {Lossless Image Compression, JPEG Collections, DCT-Domain Coding, Graph-Based Prediction, Residual Coding, Entropy Coding},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715313}
}