Visual information measurement with quality assessment
The quantity of visual information measurement is significant for many perception-oriented signal processing system. The classical Shannon theory, which based on the probability of signal, is useful to measure the quantity of channel information. However, it fails to accurately measure the quantity of visual information of a given image. Image quality refers to the subjective perception on the visual information that an image carried. An image with high quality carries more information than that of a low quality image. Thus, the quality of an image can effectively represent its quantity of visual information. In this paper, we propose a novel visual information measurement, and verify it with quality assessment. Firstly, a dictionary is learned from natural images, in which the content change of each atom is calculated to present its quantity of visual information. Then, a testing image is represented by the dictionary, and the sparse coefficients for each local block in the image are acquired. Finally, according to the sparse coefficients, the quantity of visual information is measured. The information measurement result is verified with the subjective quality score. Experimental results on a large amount of images demonstrate the accuracy of the proposed method for quantity of visual information measurement.