Human brain memory model based non-reference video quality evaluation method

Abstract

The invention discloses a human brain memory model based non-reference video quality evaluation method. The method includes: 1), dividing a frame video into micro blocks and calculating complexity of each macro block according to the brightness value of the pixel in each macro block; 2), acquiring quality evaluation values of the macro blocks by the aid of the complexity and quantization coefficient of each macro block and combining with influence of the surrounding macro blocks; 3), based on a human eye space attention model and combining movement tendency of an entire video, endowing the macro blocks different in position with different weights to acquire a human eye attention distribution graph; 4), acquiring objective quality of the frame video by combining the quality evaluation value of each macro block and the human eye attention distribution graph; 5), giving the objective quality of the video by taking an image group as a unit and combining the human brain memory model. The method is simple, the objective quality can be acquired through the to-be-estimated video, higher flexibility is achieved, accurate evaluation results for various different video scenes can be acquired, and good universality is achieved.

Description

technical field [0001] The invention belongs to the technical field of video quality evaluation, and in particular relates to a no-reference video quality evaluation method based on a human brain memory model. Background technique [0002] With the rapid development of computer and network communication technology, people's demand for multimedia information is increasingly strong. In recent years, video-related applications cover various fields, such as video conferencing, video surveillance, and mobile TV. In these applications, video information needs to be compressed and transmitted before reaching the receiver, and these processes often cause video quality loss. In order to obtain a better subjective effect, it is necessary to evaluate the video quality, and adjust the parameters of the encoder and transmission channel according to the results. The final receptor of the video is the human eye, and human eye observation is considered to be the most accurate method for e...

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Problems solved by technology

[0004] Although the existing video quality evaluation methods have achieved certain results, some relatively mature models have been formed, such as two video quality evaluation models based on PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). This model still has some problems, such as: it does not consider the factors that the human brain's attention to the video changes over time; it ignores the impact of video content features on video quality; the accuracy needs to be improved

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