Abstract
Compressive Imaging (CI) is a potential sensing technology for energy-efficient visual sensors, and its rate-distortion performance can be improved by adaptive Compressive Sensing (CS) of image. However, due to the unavailability of original image, it is a challenge for CI-based adaptive CS to extract an effective feature from measurements to evaluate the sparsity of image. In view of that, this paper presents an entropy-assisted adaptive CS system, whose merit is its definition of the sensed entropy without the original image. Based on sensed entropy, each image block is allocated sufficient measuring resources, guaranteeing a cost-effective reconstruction of image. Experimental results show that the proposed entropy-assisted adaptive CS system provides better objective and subjective recovery qualities with a low measuring and recovering complexity.
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