Image Detection With Pixel Matching for Cloud Gaming Freezes
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Solution Overview
Problem
Existing methods struggle to accurately detect game freezes in cloud gaming due to varying CPU consumption across different game scenes, leading to poor detection accuracy.
Innovation Solution
An image detection method that intercepts images at preset intervals, performs pixel matching, and conducts picture content detection to determine if the video stream is abnormal by analyzing the similarity and content of the images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If CPU usage frequency is used to detect game freezes, then the detection method is simple to implement, but the detection accuracy deteriorates due to varying CPU consumption across different game scenes
Solution Approach 1:
The patent replaces the CPU-based detection mechanism with an image processing-based mechanism. Instead of monitoring CPU usage frequency, the system intercepts game images, performs pixel matching between consecutive frames, and detects freezes based on image similarity. This substitution resolves the contradiction by using a different detection dimension (visual content stability vs. processor load) that is not affected by scene-dependent CPU variation.
Solution Approach 2:
The patent introduces image data as an intermediary between the game running state and the detection system. By intercepting game images and analyzing their pixel changes, the system creates an intermediate representation of game state stability that is independent of CPU usage patterns. This intermediary approach allows accurate freeze detection without being confounded by normal CPU consumption variations during different game scenes.
2Ease of manufacture
If a fixed threshold is set for CPU usage to determine abnormality, then the detection process is straightforward, but the detection accuracy deteriorates due to scene-specific CPU consumption variations
Solution Approach 1:
The patent transforms the detection threshold from a fixed CPU usage value to a dynamic image similarity metric. Instead of using a static CPU percentage threshold that fails to account for scene variations, the system dynamically compares pixel matching results between consecutive frames. This dynamic approach adapts to different game scenes automatically, as each scene's normal CPU consumption pattern is irrelevant when detecting freeze conditions through image stability.
Solution Approach 2:
The patent changes the detection parameter from CPU usage frequency to image pixel matching similarity. By transitioning to a different measurement parameter (image content stability vs. processor load), the system eliminates the confounding effect of scene-dependent CPU variation. The new parameter (pixel matching similarity) provides a scene-independent metric that accurately reflects whether the game is frozen regardless of what scene is currently active.
3Measurement precision
If image interception and pixel matching are performed at preset time intervals, then the detection accuracy improves by analyzing actual game content, but the processing time and computational resources increase
Solution Approach 1:
The patent implements periodic image interception at preset time intervals rather than continuous monitoring. By sampling images at regular intervals (e.g., every N seconds or every M frames), the system achieves accurate freeze detection while reducing computational overhead compared to continuous real-time analysis. This periodic sampling approach maintains detection accuracy for freeze identification while managing processing time and resource consumption.
Solution Approach 2:
The patent performs pixel matching on only the necessary portions of images (e.g., key regions or sampled pixels) rather than processing every pixel in full detail. This partial action approach reduces the computational burden of each analysis cycle while maintaining sufficient accuracy to detect freezes. By applying selective pixel matching strategies, the system balances detection precision with processing efficiency.
Data Source
AI summary
The present application provides an image detection method performed by a server. The method includes: intercepting a first image and a second image at a preset time interval from a video stream; determining a value of total matching pixels between the first image and the second image; in response to determining that the value of total matching pixels between the first image and the second image satisfies a preset matching condition, detecting content in the second image; and determining that the video stream is abnormal when no picture content is in the second image. In this way, an image recognition manner can be used to perform detection on image pictures of the video stream at the preset time interval.