Scene Change Detection in Remotely Hosted 3D Graphics
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Solution Overview
Problem
Remotely streamed 3D games face challenges in managing scene changes efficiently, leading to increased video stream bit rates or degraded visual quality due to suboptimal use of I-frames and P-frames, especially during unpredictable scene transitions.
Innovation Solution
A system that utilizes a machine learning engine, combining fuzzy logic and neural networks, to detect scene changes by analyzing hardware and software counters from the GPU during the 3D rendering phase, providing metadata to the encoder to decide between I-frame and P-frame encoding, thereby reducing bandwidth without introducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional encoder decision-making is used for scene changes, then visual quality is maintained, but bandwidth consumption increases
Solution Approach 1:
The system performs preliminary analysis of GPU counters during the rendering phase to predict upcoming scene changes before encoding occurs. This advance detection allows the encoder to prepare appropriate encoding decisions (I-frame or P-frame) in advance, optimizing bandwidth usage without compromising visual quality during unpredictable scene transitions.
Solution Approach 2:
The system implements a feedback loop where GPU counter data from the rendering phase is continuously monitored and fed to the encoding decision-making process. This real-time feedback enables dynamic adjustment of encoding decisions based on actual scene content changes, allowing the system to reduce bandwidth consumption while maintaining visual quality by making informed encoding choices.
2Loss of energy
If I-frames are used frequently to capture scene changes, then visual quality is maintained, but bandwidth consumption increases
Solution Approach 1:
The system changes the parameter of encoding decision-making by using GPU counter data to dynamically determine when I-frames versus P-frames should be used. Instead of relying on traditional motion detection or fixed intervals, the system adjusts encoding parameters based on real-time analysis of rendering phase data, improving encoding efficiency and reducing bandwidth consumption while maintaining visual quality.
3Manufacturing precision
If scene change detection is performed during encoding, then encoding decisions are improved, but latency increases
Solution Approach 1:
The system performs scene change detection during the rendering phase using GPU counters before the encoding process begins. This preliminary detection creates a head start, allowing the encoder to receive advance notice of upcoming scene changes and make informed decisions without adding latency to the encoding process itself.
Solution Approach 2:
The system introduces GPU counter data as an intermediary between the rendering phase and encoding process. This intermediary provides scene change information to the encoder without requiring the encoder to perform additional analysis, thereby improving encoding decision accuracy while avoiding latency penalties.
Data Source
AI summary
Apparatus and method for detecting scene changes using data sets gathered from a 3D pipeline. For example, one embodiment of an apparatus comprises: a 3D graphics engine to render a plurality of frames including a current frame and a prior frame; and a scene change detector to retrieve data sets from the 3D graphics engine, the data sets associated with the current frame rendered by the 3D graphics engine, the scene change detector to analyze the data sets in view of corresponding data sets from the prior frame to determine a first type of encoding to be used for encoding the current frame prior to transmission over a network.