Video Coding Efficiency via Scene Classification
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Solution Overview
Problem
Current video processing technologies face challenges in efficiently classifying and coding video information, particularly in identifying scene changes and adapting coding parameters based on dynamic visual content, leading to suboptimal bit rate allocation and video quality.
Innovation Solution
A method that involves classifying frames into scenes such as camera zoom, fade, and pan scenes using motion estimation and compensation parameters, and adjusting coding parameters to optimize bit allocation and filtering, thereby improving video coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If motion estimation and compensation parameters are used to classify frames into scenes, then video coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent performs motion estimation and compensation parameter calculation in advance during the encoding process, using these pre-computed parameters to classify frames into different scene types (zoom, pan, fade, flash). This preliminary action enables subsequent coding decisions to be made more efficiently without requiring complex real-time analysis.
Solution Approach 2:
The patent changes coding parameters based on scene classification results. Different coding strategies are applied to different scene types: for example, intra-coded frames are specified for certain scenes, and reference frame selection is adjusted based on the detected scene type, thereby optimizing coding efficiency for each scene category.
2Quantity of substance
If coding parameters are adjusted based on scene classification, then bit rate allocation is optimized, but manufacturing precision requirements increase
Solution Approach 1:
The patent applies different coding parameters to different local regions (scenes) within the video sequence. By classifying frames into specific scene types (zoom, pan, fade, flash), the system tailors coding strategies to local characteristics, allocating bit rates appropriately for each scene type rather than using uniform coding parameters throughout.
Solution Approach 2:
The patent dynamically adjusts coding parameters based on the detected scene type. The coding strategy changes adaptively as the video content transitions between different scene types, with parameters such as intra-frame coding frequency and reference frame selection being modified in real-time according to the current scene classification.
3Measurement precision
If scene changes are identified using prediction error metrics, then scene classification accuracy is improved, but loss of information increases
Solution Approach 1:
The patent uses motion estimation and compensation parameters as intermediary variables to detect scene changes. Instead of directly analyzing raw prediction errors which may contain noise and false positives, the system uses the motion parameters (which are already computed for coding purposes) as a more reliable intermediate indicator of scene transitions, thereby improving classification accuracy while minimizing information loss.
Data Source
AI summary
Systems, methods, and techniques for treating video information are described. In one implementation, a method includes receiving video information, classifying one or more frames in the received video information as a scene, adjusting one or more coding parameters based on the classification of the frames, and coding the video information in accordance with the adjusted coding parameters.