Content-Aware Coding Tool Selection for Low-Complexity Video Encoders
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Solution Overview
Problem
Existing video compression technologies face challenges in reducing encoder complexity while maintaining video quality, as certain coding tools are not suited for all types of content, leading to inefficiencies and quality loss.
Innovation Solution
A lightweight adaptive coding tool selection system that classifies content as screen or natural using 8x8 blocks statistics, adjusting coding tools accordingly to optimize encoding efficiency and reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If coding tools are selected without content classification, then encoder complexity is reduced, but video quality deteriorates due to using unsuitable coding tools for specific content types
Solution Approach 1:
The patent performs content classification before encoding to determine the appropriate coding tools in advance. By analyzing the content type (screen or natural) before the encoding process, the system pre-selects suitable coding tools, avoiding the need for complex real-time analysis during encoding while ensuring quality through proper tool selection.
Solution Approach 2:
The patent dynamically adapts coding tool selection based on content classification results. The encoder adjusts its behavior according to whether the content is identified as screen or natural, enabling flexible adaptation to different content types without requiring a completely different encoder for each type, thus balancing complexity and quality.
2Adaptability or versatility
If coding tools are over-engineered to handle all content types, then adaptability improves, but encoder complexity increases
Solution Approach 1:
The patent creates a universal content classification mechanism that works across different content types (screen and natural) using a single classification framework. This universal approach allows one encoder to handle multiple content types by first classifying the content and then selecting appropriate tools, rather than requiring separate specialized encoders for each content type.
Solution Approach 2:
By performing content classification as a preliminary step before encoding, the system determines the appropriate coding tools in advance. This pre-analysis approach allows the encoder to maintain simplicity while achieving high adaptability, as the classification result guides tool selection rather than requiring the encoder to inherently understand and handle all content types complexly.
3Measurement precision
If content classification is performed using complex analysis methods, then classification accuracy improves, but processing time increases
Solution Approach 1:
The patent employs simple, lightweight statistical methods (such as analyzing the number of non-zero pixels or color distribution in small blocks) rather than complex computational models for content classification. These simple metrics provide sufficient accuracy to distinguish between screen and natural content while requiring minimal processing time, avoiding the use of heavy computational resources.
Solution Approach 2:
The patent changes the parameters used for classification from complex multi-dimensional analyses to simpler statistical metrics. By focusing on basic parameters like pixel non-zero counts, color histograms, or block-level statistics, the system achieves adequate classification accuracy with significantly reduced computational complexity and processing time compared to more sophisticated analysis methods.
Data Source
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AI summary
A lightweight but effective adaptive coding tool selection system with content classification can be implemented to reduce complexity and maintain quality in a video encoder. Content classification may classify a current frame between at least three classifications: screen content, weak screen content, and natural content. Content classification may make use of two statistics, e.g., color number and variance, of blocks that are 8x8 pixels or larger in size. The statistics may be used to calculate three frame-level statistics, e.g., proportion/percentage of blocks with few colors, proportion/percentage of blocks with zero variance, and proportion/percentage of blocks with big/large variance. The frame-level statistics are used to classify the current frame. Based on the classification, coding tool control flags or control signals may be generated accordingly to configure the encoder to, e.g., turn on or off certain coding tools, and/or use certain parameter values for the coding tools.