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A deep learning based multi-image compression technique

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Abstract

A multi-image compression technique compresses multiple images of the same or various sizes together to generate a common codebook. In multi-image compression, the size of the common codebook or code vector matrix formed from multiple images is crucial to the algorithm's compression ratio performance. This codebook comprises codewords created by the multi-Image compression technique after various tuning settings have been modified. The compression ratio of the multi-image compression approach can be improved even more by lowering the size of the common code vector matrix. The common codebook or code vector matrix is reduced in size in this study using deep learning based auto-encoder technology. The encoded matrix is substantially smaller than the matrix formed using standard encoding techniques. For decoding purposes, information on the number of neurons and layers employed during encoding is also stored. The suggested approach is tested on a large number of standard photos and images from the UCID version 2 database. The experimental results are examined using compression ratio, PSNR, and SSIM. The results demonstrate that the suggested technique decreases the size of the common code vector matrix or codebook by 20%, improving overall algorithm performance by about 1.5% while maintaining the visual quality of the decompressed images.

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This research receives no specific grant from any funding agency in the public, commercial, or not-for-profit sector.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Government College of Engineering and Textile Technology, Berhampore, West Bengal, India

    Dibyendu Barman, Abul Hasnat & Shemim Begum

  2. Department of Electronics and Communication Engineering, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, India

    Bandana Barman

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  1. Dibyendu Barman
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  2. Abul Hasnat
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  3. Shemim Begum
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  4. Bandana Barman
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Contributions

The study was conceived and designed by all of the authors. DB designed the experiments, collected and analyzed the data, and wrote the paper. AH and BB who are PhD guides of DB and SB helped to revise the manuscript. All authors agreed to be held accountable for the content of the final version of the manuscript.

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Correspondence to Dibyendu Barman.

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Barman, D., Hasnat, A., Begum, S. et al. A deep learning based multi-image compression technique. SIViP 18 (Suppl 1), 407–416 (2024). https://doi.org/10.1007/s11760-024-03163-8

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  • DOI: https://doi.org/10.1007/s11760-024-03163-8

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