Name
Abstract | ||
|---|---|---|
In this paper, we propose a lossy image compression algorithm called microshift. We employ an algorithm-hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, and then the sub-quantized values are further compressed. Two methods, FAST and MRF models, are proposed to recover the bitdepth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. On an average, our compression algorithm can compress images to 1.25-bits per pixel with a resulting quality that outperforms the state-of-the-art on-chip compression algorithms in both peak signal-to-noise ratio and structual similarity. Then, we propose a hardware architecture and implement the algorithm on an FPGA. The results on the ASIC design further validate the low-hardware complexity and high-power efficiency, showing that our method is promising, particularly for low-power wireless vision sensor networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tcsvt.2018.2880227 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | Field | DocType |
Image coding,Uncertainty,Hardware,Compression algorithms,Quantization (signal),Complexity theory,Image quality | Computer vision,Spatial correlation,Image sensor,Computer science,Field-programmable gate array,Image quality,Application-specific integrated circuit,Pixel,Artificial intelligence,Data compression,Hardware architecture | Journal |
Volume | Issue | ISSN |
29 | 11 | 1051-8215 |
Citations | PageRank | References |
3 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Zhang | 1 | 22 | 5.68 |
| Pedro V. Sander | 2 | 1111 | 63.92 |
| Chi-ying Tsui | 3 | 1939 | 314.94 |
| Amine Bermak | 4 | 493 | 90.25 |