Distributed compressed video sensing based on convolutional sparse coding using Fourier measurement matrix and L1 fidelity term

Takuro Eguchi; Yudai Gondo; Yoshimitsu Kuroki

doi:10.1117/12.3019408

2 May 2024 Distributed compressed video sensing based on convolutional sparse coding using Fourier measurement matrix and L1 fidelity term

Takuro Eguchi, Yudai Gondo, Yoshimitsu Kuroki

Author Affiliations +

Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 131642T (2024) https://doi.org/10.1117/12.3019408
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia

Abstract

This paper investigates Distributed Compressed Video Sensing (DCVS) using Convolutional Sparse Coding (CSC). DCVS is a coding method that combines compressed video sensing and distributed video coding. Although many CSC approaches to DCVS use a random matrix as the measurement matrix, our method employs the Fourier matrix for the measurement matrix to reduce the computational load and memory consumption. In addition, this work addresses a DCVS in the convolutional sparse coding manner, which is more robust against object shift than that in a block-wise manner. The convolutional filters are also designed to improve the fidelity by using the L1 fidelity term. The experiments show that the proposed method outperforms the conventional method in SSIM and PSNR while reducing the execution time and memory usage at both the encoders and the decoder’s sides.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Takuro Eguchi, Yudai Gondo, and Yoshimitsu Kuroki "Distributed compressed video sensing based on convolutional sparse coding using Fourier measurement matrix and L1 fidelity term", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 131642T (2 May 2024); https://doi.org/10.1117/12.3019408

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available