Video Encoding Quantization via Content Category Similarity
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Solution Overview
Problem
Determining optimal quantization parameters for video encoding is computationally intensive, particularly in systems with power limitations, such as mobile devices, as it affects both bitrate and distortion, and existing methods require complex calculations.
Innovation Solution
A video encoding method that determines weights for video image blocks based on similarity to content categories, sets quantization precision indicators as a linear combination of content-category-level indicators, and adjusts block-level encoding parameters using content-category-level extensions, allowing for efficient encoding by interpolating quantization parameters based on target rates and similarity weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex calculations are used to determine optimal quantization parameters, then encoding quality (bitrate and distortion) is improved, but computational complexity and power consumption increase
Solution Approach 1:
The patent changes the parameters used for quantization by introducing content-category-based quantization parameters (QP_cat) that replace complex block-level rate-distortion optimization calculations. This allows the system to achieve good encoding quality without the heavy computational burden of traditional methods by using simplified parameter determination based on content category classification.
Solution Approach 2:
The patent segments the video image into different content categories (e.g., sky, water, grass, snow, ice, sand, rock, building, vehicle, person, animal, plant, other) and applies different quantization parameters to each category. This segmentation approach simplifies the overall quantization process by handling different regions with category-specific parameters rather than using complex global optimization.
2Measurement precision
If traditional quantization parameter determination methods are used, then encoding accuracy is maintained, but processing time and energy consumption increase
Solution Approach 1:
The patent determines quantization parameters based on content category classification rather than performing computationally intensive rate-distortion optimization. By changing the parameter determination approach to use category-based lookup tables and simple interpolation, the system maintains encoding accuracy while significantly reducing power consumption, especially important for mobile devices.
Solution Approach 2:
The patent performs content category classification and determines quantization parameters in advance before the actual encoding process. By pre-determining the QP values based on the identified content categories, the system avoids the need for complex real-time calculations during encoding, thereby reducing processing time and energy consumption while maintaining accuracy.
Data Source
AI summary
In some embodiments, content-category-level encoding statistical indicators (statistics) are assigned to weighted linear combinations of corresponding macroblock-level statistics. Content categories may identify potentially overlapping content types such as sky, water, grass, skin, and red content. The combination weights may be similarity measures describing macroblock similarities to content categories. A given macroblock may be associated with multiple content categories, with different similarity levels for different content categories. A similarity measure for a given macroblock with respect to a content category may be defined as a number (between 0 and 8) of neighboring macroblocks that meet a similarity condition, provided the macroblock meets a qualification condition. The similarity condition may be computationally simpler than the qualification condition. Macroblock-level encoding parameters are generated by combining content-category-level parameters.