A no-reference video quality assessment method based on feature fusion and recurrent neural network

A technology of cyclic neural network and feature fusion, which is applied in TV, electrical components, image communication, etc., can solve problems such as poor quality evaluation performance, and achieve the effect of large detection quality range, accurate quality evaluation, and accurate quality evaluation indicators.
CN110677639BActive Publication Date: 2021-06-11COMMUNICATION UNIVERSITY OF CHINA
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
COMMUNICATION UNIVERSITY OF CHINA
Publication Date
2021-06-11

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Abstract

The invention discloses a no-reference video quality evaluation method based on feature fusion and cyclic neural network. The method fuses spatio-temporal features through a feature fusion network that can input video segments, and uses a cyclic neural network to fuse the quality of different video segments to complete the process. Overall video quality assessment task. The neural network used in the present invention directly uses video segments as input and adopts a feature fusion network. This design can better extract the direct relationship between video frames, thereby obtaining the overall quality evaluation index of the video more accurately. The feature fusion network of the present invention can process multiple frames at one time and obtain a low-dimensional feature, that is, the feature scale is greatly reduced compared with the amount of data, and the total time can be obtained extremely quickly for a whole section of video during operation. big reduction.
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Description

technical field

[0001] The invention relates to a no-reference video quality evaluation method based on feature fusion and cyclic neural network, belonging to the technical field of digital video processing. Background technique

[0002] As a complex source of visual information, video contains a lot of valuable information. The quality of video directly affects people's subjective feelings and information acquisition, and can be used to measure other video tasks such as video compression. The research on Video Quality Assessment (VQA) has also received extensive attention in recent years.

[0003] Video quality evaluation can be divided into subjective evaluation methods and objective evaluation methods. Subjective evaluation involves observers subjectively scoring the video quality, but the subjective evaluation workload is heavy, time-consuming, and inconvenient; the objective evaluation method is to calculate the quality index of the video by the computer according to a...

Claims

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