research-article

Principal Component Approximation Network for Image Compression

Authors:

Anup BasuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 5

Article No.: 121, Pages 1 - 20

https://doi.org/10.1145/3637490

Published: 11 January 2024 Publication History

Abstract

In this work, we propose a novel principal component approximation network (PCANet) for image compression. The proposed network is based on the assumption that a set of images can be decomposed into several shared feature matrices, and an image can be reconstructed by the weighted sum of these matrices. The proposed PCANet is specifically devised to learn and approximate these feature matrices and weight vectors, which are used to encode images for compression. Unlike previous deep learning-based methods, a distinctive aspect of our approach is its consideration of network size in the bit-rate computation. Despite this inclusion, our proposed method yields promising results. Through extensive experiments conducted on standard datasets, we demonstrate the effectiveness of our approach in comparison to state-of-the-art techniques. To the best of our knowledge, this is the first machine learning approach that includes the size of networks during bitrate computation in image compression.

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Cited By

Renuka GVidhusha S(2024)Multifaceted Approaches for Facial Image Compression: A Review on State of Art Techniques and Applications2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT61487.2024.10580030(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICITIIT61487.2024.10580030
Shao ZLi LLi BShang YCoatrieux GShu HWang C(2024)Quaternion-based 2D-DOST and Stacked Principal Component Analysis Network for Multimodal Face RecognitionApplied Soft Computing10.1016/j.asoc.2024.112154(112154)Online publication date: Aug-2024
https://doi.org/10.1016/j.asoc.2024.112154

Index Terms

Principal Component Approximation Network for Image Compression
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 5

May 2024

650 pages

EISSN:1551-6865

DOI:10.1145/3613634

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2024

Online AM: 13 December 2023

Accepted: 10 December 2023

Revised: 09 November 2023

Received: 14 August 2023

Published in TOMM Volume 20, Issue 5

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Cited By

Renuka GVidhusha S(2024)Multifaceted Approaches for Facial Image Compression: A Review on State of Art Techniques and Applications2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT61487.2024.10580030(1-6)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICITIIT61487.2024.10580030
Shao ZLi LLi BShang YCoatrieux GShu HWang C(2024)Quaternion-based 2D-DOST and Stacked Principal Component Analysis Network for Multimodal Face RecognitionApplied Soft Computing10.1016/j.asoc.2024.112154(112154)Online publication date: Aug-2024
https://doi.org/10.1016/j.asoc.2024.112154

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