Adaptive Sampling for Computer Vision-Oriented Compressive Sensing.

Compressive sensing (CS) is renowned for its efficient signal data compression. However, due to its compressive nature, the accuracy of downstream computer vision (CV) tasks by reconstruction inevitably degrades as sampling rate decreases. This limitation significantly hinders the application of existing CS techniques. To overcome the drawback, this paper presents a novel CS technique that employs adaptive sampling rates based on saliency distribution. The goal of this work is to enhance the preservation of information necessary for classification while reducing the weight of non-essential information. Experimental results show the effectiveness of the proposed adaptive sampling technique, which outperforms existing sampling CS techniques on STL10 and Imagenette datasets. The average classification accuracy is maximally improved by 26.23% and 18.25%, respectively.