Scalable Coding for High-Resolution, High-Compression Ratio Snapshot Compressive Video

High-speed cameras are crucial for capturing fast events beyond human perception, although challenges in terms of storage, bandwidth, and cost hinder their widespread use. As an alternative, snapshot compressive video can overcome these challenges by exploiting the principles of compressed sensing t...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 34; pp. 3960 - 3970
Main Authors: Guzman, Felipe, Diaz, Nelson, Romero, Bastian, Vera, Esteban
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Apertures
Cacti
compressed sensing
Compression ratio
Dispersion
High speed cameras
High-compressive video
high-resolution video
Image coding
Image reconstruction
Inverse problems
Kernel
Machine learning
Multiplexing
Reconstruction algorithms
Sampling
Scalability
scalable coded aperture
snapshot compressive video
Spatial resolution
Temporal resolution
Time division multiplexing
Training
Video compression
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:High-speed cameras are crucial for capturing fast events beyond human perception, although challenges in terms of storage, bandwidth, and cost hinder their widespread use. As an alternative, snapshot compressive video can overcome these challenges by exploiting the principles of compressed sensing to capture compressive projections of dynamic scenes into a single image, which is then used to recover the underlying video by solving an ill-posed inverse problem. However, scalability in terms of spatial and temporal resolution is limited for both acquisition and reconstruction. In this work, we leverage time-division multiplexing to design a versatile scalable coded aperture approach that allows unseen spatio-temporal scalability for snapshot compressive video, offering on-the-fly, high-compression ratios with minimal computational burden and low memory requirements. The proposed sampling scheme is universal and compatible with any compressive temporal imaging sampling matrices and reconstruction algorithm aimed for low spatio-temporal resolutions. Simulations validated with a series of experimental results confirm that we can compress up to 512 frames of 2K <inline-formula> <tex-math notation="LaTeX">\times 2 </tex-math></inline-formula>K resolution into a single snapshot, equivalent to a compression ratio of 0.2%, delivering an overall reconstruction quality exceeding 30 dB in PSNR for conventional reconstruction algorithms, and often surpassing 36 dB when utilizing the latest state-of-the-art deep learning reconstruction algorithms. The results presented in this paper can be reproduced in the following GitHub repository: https://github.com/FOGuzman/All-scalable-CACTI
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2025.3579208