Video quality improving method based on sensitive information enhancement for video monitoring system

Abstract

The invention relates to a video quality improving method based on sensitive information enhancement for a video monitoring system. The video quality improving method is characterized by comprising the following steps: calculating a coded quantization parameter value according to the parameter setting of a coder; acquiring a pixel average SATD (Sum of Absolute Transformed Difference) value; calculating the SATD value of chromaticity to obtain the pixel average SATD value of the chromaticity; calculating a chromaticity quantization parameter through a quantization parameter difference value factor; performing extra compensation coding on video brightness by adopting front-end equipment of a video optimizer to improve the image quality. According to the video quality improving method based on sensitive information enhancement for the video monitoring system, video data are acquired to calculate the coded quantization parameter value, a chromaticity component code rate is lowered appropriately, and a brightness component code rate is increased, so that better image quality can be obtained under the condition of the same code rate.

Description

technical field [0001] The invention relates to a method for improving video quality, in particular to a method for improving video quality of a video surveillance system based on sensitive information enhancement. Background technique [0002] A large number of studies have shown that the sensitivity of the human visual system to brightness is greater than that of chromaticity. Compared with brightness, chromaticity can use a relatively low resolution without affecting the visual effect of the image. Therefore, the sampling format using the YUV chroma space is usually 4:2:0, and both chroma components are half of the luma component in terms of horizontal and vertical resolution. In the process of inter-frame predictive coding, the H.264 / AVC video coding standard stipulates that luma components are used for motion estimation, and chrominance motion vectors are derived from corresponding luma motion vectors. In the sub-pixel domain, due to the difference in spatial sampling ...

AI Technical Summary

Problems solved by technology

The difference in sampling rate is to take into account the visual characteristics of the human eye. Similarly, the PSNR value of the chrominance component is much higher than the brightness, which will not improve the image quality much, but will cause a certain degree of bit rate waste.

[0003] like figure 1 As shown, the distribution of existing video surveillance systems is roughly the same, but due to processing defects, the resulting video quality is often poor, and better monitoring cannot be performed.

Images