Video Automatic Tracking System Imaging Quality Evaluation

With the rapid development of digital image processing and digital video technology, the assessment of the image or video quality the system can reproduce become one of the important parameters by which we can evaluate the performance of a digital imaging system. However, there is no uniform standard of image quality assessment at present. So researches on assessment of digital image quality have important application value, and already become one of the basic technology of the image processing engineering.The paper is based on image processing of the video frequency tracking system. It introduces image processing of the video frequency tracking system, and summarizes the status and developing process of image quality assessment at first. After series of analysis of cases at representative image quality assessment in the different phase, we find that subjective assessment is too fussy and cannot repeat, while the results of objective assessments are not tally with the fact image quality. So using the properties of human visual system (HVS) is an effective approach to settle this problem and the trend of the research field of the image quality assessment.Then the paper analysis the inherent connection between human visual system and discrete wavelet transform (DWT) by the numbers, and makes a point according as the combining of them. The paper adopts four level wavelets to decompose image. An important question that has no conclusion yet, the selection of wavelet bases in the practical application, is also discussed and summarized, and the fit wavelet base function of the new algorithm is selected.Finally, an improved algorithm of image quality assessment is presented, which is Vision and Weighted Signal-to-Noise Ratio (VWSNR). In terms of series of analysis of cases, its evaluation effect will be more comprehensive and consistent with human perception than the traditional methods, especially aims at image that partly disturbed. And it is proved convenient, feasible and accurate. The paper also presents a new algorithm for assessing video frequency image clarity based on VWSNR, which is proved practical in practice.