Video packet loss synchronous compensation method and system based on deep learning

By combining the deep learning LSTM and GAN network with the attention-gated video packet loss synchronization compensation method, the problems of temporal continuity and detail features caused by video packet loss are solved, and high-quality video synchronization compensation effect is achieved.

CN120640087AActive Publication Date: 2025-09-12SHANGHAI MINGQI NETWORK TECH CO LTD
7 Cites 3 Cited by

Patent Information

Application Number
CN202511120274.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-12
Publication Date
2025-09-12
Estimated Expiration
2045-08-12

AI Technical Summary

Technical Problem

Existing video packet loss compensation methods are difficult to restore the temporal continuity and detailed features of lost frames, resulting in poor synchronization between the compensated frames and the original video stream and blurred images.

Method used

A deep learning-based video packet loss synchronization compensation method is adopted. Through the LSTM prediction network and the GAN generator network, combined with the attention-gated long short-term memory component, inter-frame motion mapping, region enhancement mapping and feature fusion are performed to generate alternative frame features and finally generate synchronization compensation frames.

Benefits of technology

It improves the timing consistency and detail accuracy of the compensation frames, solves the synchronization compensation problem in video packet loss scenarios, and ensures video smoothness and synchronization accuracy.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a video packet loss synchronous compensation method and system based on deep learning, and the method comprises the steps: obtaining a to-be-compensated video stream, extracting the features of an original video frame, loading the features to a packet loss compensation model containing an LSTM prediction network and a GAN generator network, and enabling a time sequence prediction unit to comprise a long short-term memory assembly with attention gating; the LSTM prediction network sequentially performs inter-frame motion mapping, region enhancement mapping and region feature fusion on the original video frame features through a time sequence prediction unit to obtain a prediction feature sequence; and the GAN generator generates and decodes the prediction feature sequence to obtain a target replacement frame feature, and further obtains a synchronous compensation frame from the video stream to be compensated. According to the method, time sequence state adjustment is guided through attention gating, cooperation of time sequence prediction and detail generation is realized in combination with LSTM and GAN, time sequence coherence and detail accuracy of compensation frames are improved, and the problem of synchronous compensation in a video packet loss scene is effectively solved.
Need to check novelty before this filing date? Find Prior Art