Cloud Gaming Video Fluency Detection via Pixel Digital Frame Mask
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Solution Overview
Problem
Existing image processing methods for evaluating the fluency of cloud gaming video streams face challenges in accurately detecting frame sequence numbers due to noise and complexity in game pictures, leading to reduced accuracy in fluency evaluation.
Innovation Solution
The method involves generating a pixel digital frame mask with preset pixel position sets for each frame of the video stream, determining target preset pixel position sets based on pixel values, and using these sets to accurately determine frame sequence numbers, thereby assessing video fluency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to detect frame sequence numbers in cloud gaming video streams, then the processing can be performed on game pictures, but the accuracy of frame sequence number detection deteriorates due to noise and complexity in game pictures
Solution Approach 1:
The patent divides the frame sequence number detection task into multiple independent preset pixel position sets. Each preset pixel position set corresponds to a specific digit position in the frame sequence number. By segmenting the detection task across multiple fixed positions, the system can accurately identify each digit independently, overcoming the interference from noise and game picture complexity. The processing circuitry sequentially checks pixels at these predetermined positions to reconstruct the complete frame sequence number.
2Reliability
If frame dropping processing is performed repeatedly to evaluate fluency, then the fluency evaluation can be conducted, but the time required for evaluation increases
Solution Approach 1:
The patent performs preliminary action by pre-defining multiple preset pixel position sets before the actual fluency evaluation begins. These preset positions are established in advance and stored in the processing circuitry. During fluency evaluation, the system can immediately access and check these pre-configured positions without performing complex detection algorithms repeatedly, significantly reducing the time required for each fluency assessment while maintaining high accuracy.
Data Source
AI summary
An image processing method is provided. For each frame of a video stream, a pixel digital frame mask in the respective frame of the video stream is obtained. The pixel digital frame mask of the respective frame includes a plurality of preset pixel position sets. At least two target preset pixel position sets are determined from the plurality of preset pixel position sets that form a frame sequence number of the respective frame based on values of pixels included in the at least two target preset pixel position sets. A frame sequence number corresponding to the respective frame of the video stream is determined according to positions of the at least two target preset pixel position sets in the pixel digital frame mask in the respective frame. Further, video fluency of the video stream is determined based on the frame sequence numbers.