No-reference video quality evaluation method based on feature fusion and recurrent neural network

A technology of cyclic neural network and feature fusion, applied in television, electrical components, image communication, etc., can solve problems such as poor quality evaluation performance, and achieve the effect of large-scale and accurate quality evaluation
CN110677639AActive Publication Date: 2020-01-10COMMUNICATION UNIVERSITY OF CHINA
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
COMMUNICATION UNIVERSITY OF CHINA
Publication Date
2020-01-10

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Abstract

The invention discloses a no-reference video quality evaluation method based on feature fusion and a recurrent neural network, and the method achieves the fusion of spatial and temporal features through a feature fusion network which can input video segments, achieves the fusion of the quality of different video segments through the recurrent neural network, and completes the overall quality evaluation task of the video. According to the neural network used in the method, the video segments are directly used as input, and the feature fusion network is adopted, so that the direct relationship of the video frames can be better extracted, and the overall quality evaluation index of the video can be more accurately obtained. The feature fusion network can process multiple frames at a time andobtain a low-dimensional feature, that is, the feature scale is greatly reduced relative to the data volume, and the total time can be greatly reduced for a whole video during the operation process.
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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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