Game frame rate optimization method and system, electronic equipment and storage medium

By collecting graphics buffers and metadata, and using a multi-complexity model library to predict and generate compensation frames, the problem of unstable frame rates in mobile game has been solved, improving user experience and frame rate stability.

CN121819318APending Publication Date: 2026-04-10SHANGHAI LONGCHEER TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-25
Publication Date
2026-04-10

AI Technical Summary

Technical Problem

When running games on mobile devices, existing technologies struggle to maintain a stable frame rate, often resulting in dropped frames and a degraded user experience. Furthermore, existing solutions lack a deep understanding and predictive ability regarding the game rendering process, making intelligent optimization impossible.

Method used

The system collects the rendered graphics buffer and its associated metadata, uses multiple machine learning model libraries of varying complexity to predict the image content of subsequent frames, and dynamically selects the appropriate model based on the duration of frame loss and the confidence score of the model output to generate compensation frames to fill in the frame loss.

Benefits of technology

By generating high-quality compensation frames, the stability of game frame rates and user experience are significantly improved, achieving a balance between performance and quality. It adapts to different system loads and frame drop scenarios, and improves the accuracy and real-time performance of predictions.

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Abstract

The invention relates to the technical field of graphic rendering, and discloses a game frame rate optimization method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting a rendered graphic buffer area and associated metadata, and obtaining a game frame rate based on the collected graphic buffer area and metadata; predicting image contents of subsequent frames by using a machine learning model library comprising a plurality of models with different complexities, and dynamically selecting an adaptive model according to frame loss duration and confidence scores output by the models; when frame loss is detected, generating a compensation frame according to the predicted image content; and displaying and outputting the compensation frame. Through multi-dimensional metadata collection, hierarchical model matrix design, a dynamic model selection strategy and game feature recognition and special model adaptation, the frame loss problem in game operation is solved, game fluency and user experience are improved, and meanwhile good cross-platform compatibility and system stability are achieved.
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