Name
Title | ||
|---|---|---|
Measurement-Domain Spiral Predictive Coding For Block-Based Image Compressive Sensing |
Abstract | ||
|---|---|---|
To improve the rate-distortion performance of block-based image compressive sensing and reduce the influence of image edges, this paper proposes a measurement-domain spiral predictive coding method, which can make full use of the intrinsic spatial relationship of natural images. For the measurements of each compressive-sensing block, the optimal measurement prediction is selected from a set of measurement prediction candidates that are generated by eight possible directional prediction modes. Then, the resulting residual is processed by scalar quantization. The block prediction starts from the center of an image and spreads around in spiral scanning, where each block can select the optimal measurement prediction from the measurement prediction candidates as many as possible. The experimental results show that the proposed method can achieve the best rate-distortion performance as compared with the existing methods, while the complexity is basically similar. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-34113-8_1 | IMAGE AND GRAPHICS, ICIG 2019, PT III |
Keywords | DocType | Volume |
Spiral predictive coding, Measurement prediction, Scalar quantization, Rate-distortion | Conference | 11903 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 2 | |