期刊
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
卷 2017, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2017/9059204
关键词
-
资金
- National Natural Science Foundation of China [61501393, 61601396]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences [17YJCZH123]
- Key Scientific Research Project of Colleges and Universities in Henan Province of China [16A520069]
Compressive Sensing (CS) realizes a low-complex image encoding architecture, which is suitable for resource-constrained wireless sensor networks. However, due to the nonstationary statistics of images, images reconstructed by the CS-based codec have many blocking artifacts and blurs. To overcome these negative effects, we propose an Adaptive Block Compressive Sensing (ABCS) system based on spatial entropy. Spatial entropy measures the amount of information, which is used to allocate measuring resources to various regions. The scheme takes spatial entropy into consideration because rich information means more edges and textures. To reduce the computational complexity of decoding, a linear mode is used to reconstruct each block by the matrix-vector product. Experimental results show that our ABCS coding system provides a better reconstruction quality from both subjective and objective points of view, and it also has a low decoding complexity.
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