Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Block based Adaptive Compressive Sensing with Sampling Rate Control

Kosuke Iwama, Ryugo Morita, Jinjia Zhou

TL;DR

This paper explores the temporal redundancy in video CS and proposes a block based adaptive compressive sensing framework with a sampling rate (SR) control strategy that is able to control SR and obtain better performance than existing works.

Abstract

Compressive sensing (CS), acquiring and reconstructing signals below the Nyquist rate, has great potential in image and video acquisition to exploit data redundancy and greatly reduce the amount of sampled data. To further reduce the sampled data while keeping the video quality, this paper explores the temporal redundancy in video CS and proposes a block based adaptive compressive sensing framework with a sampling rate (SR) control strategy. To avoid redundant compression of non-moving regions, we first incorporate moving block detection between consecutive frames, and only transmit the measurements of moving blocks. The non-moving regions are reconstructed from the previous frame. In addition, we propose a block storage system and a dynamic threshold to achieve adaptive SR allocation to each frame based on the area of moving regions and target SR for controlling the average SR within the target SR. Finally, to reduce blocking artifacts and improve reconstruction quality, we adopt a cooperative reconstruction of the moving and non-moving blocks by referring to the measurements of the non-moving blocks from the previous frame. Extensive experiments have demonstrated that this work is able to control SR and obtain better performance than existing works.

Block based Adaptive Compressive Sensing with Sampling Rate Control

TL;DR

This paper explores the temporal redundancy in video CS and proposes a block based adaptive compressive sensing framework with a sampling rate (SR) control strategy that is able to control SR and obtain better performance than existing works.

Abstract

Compressive sensing (CS), acquiring and reconstructing signals below the Nyquist rate, has great potential in image and video acquisition to exploit data redundancy and greatly reduce the amount of sampled data. To further reduce the sampled data while keeping the video quality, this paper explores the temporal redundancy in video CS and proposes a block based adaptive compressive sensing framework with a sampling rate (SR) control strategy. To avoid redundant compression of non-moving regions, we first incorporate moving block detection between consecutive frames, and only transmit the measurements of moving blocks. The non-moving regions are reconstructed from the previous frame. In addition, we propose a block storage system and a dynamic threshold to achieve adaptive SR allocation to each frame based on the area of moving regions and target SR for controlling the average SR within the target SR. Finally, to reduce blocking artifacts and improve reconstruction quality, we adopt a cooperative reconstruction of the moving and non-moving blocks by referring to the measurements of the non-moving blocks from the previous frame. Extensive experiments have demonstrated that this work is able to control SR and obtain better performance than existing works.

Paper Structure

This paper contains 12 sections, 7 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. Overall framework
  4. Moving Block Detection
  5. SR Control with Dynamic Threshold
  6. Block storage system
  7. Dynamic threshold
  8. Experiment
  9. Dataset and Implementation Details
  10. Comparison with State-of-the-Art Methods
  11. Analysis and Discussion
  12. Conclusion

Figures (5)

  • Figure 1: The framework of our proposed method consists of sensing and reconstruction. The sensing part includes measurements-based moving block detection and a sampling rate controller. The reconstruction part includes a method to refer to the non-moving blocks and deep learning based image reconstruction. Each frame is divided into non-overlapping $B \times B$ blocks, and sampling processing is performed block by block.
  • Figure 2: Illustration of Moving Block Detection
  • Figure 3: Comparison of reconstruction performance among state-of-the-art image CS methods CASNet chen2022content and FSOINet chen2022fsoinet and our method on various target SR.
  • Figure 4: Visual comparison of different CS methods on frame 619 of the 'S_0401' sequence and frame 255 of the 'S_0102' sequence and frame 225 of the 'S_0101' sequence. CASNet and FSOINet are set to SR = 4.00%, and the other methods are set to the same values as in Table \ref{['tab:sota']}.
  • Figure 5: The transition curves for the number of moving blocks, block storage, threshold, and sampling rate, respectively on 'S_0102' sequence at $SR_{\text{t}}$ = 1.00%.