Video Encoding Mode Decisions by 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, and affects both bitrate and distortion characteristics.
Innovation Solution
A video encoding method that determines weights for video image blocks based on their similarity to content categories, sets quantization precision indicators as a linear combination of content-category-level indicators, and encodes blocks using these indicators, allowing for efficient bitrate control and distortion management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If computationally intensive methods are used to determine optimal quantization parameters, then bitrate and distortion characteristics are optimized, but computational complexity and power consumption increase
Solution Approach 1:
The video image is divided into multiple blocks, and each block is independently classified into content categories (e.g., sky, water, grass, foliage, building, road, vehicle, person). This segmentation allows the system to apply different quantization parameters to different blocks based on their content type, reducing the need for computationally intensive global optimization while achieving localized precision improvements.
Solution Approach 2:
Different quantization parameters are assigned to different blocks based on their content category classification. This local quality approach allows the system to optimize quantization parameters locally for each block type rather than using a uniform approach, improving distortion characteristics for specific content while reducing overall computational complexity.
2Manufacturing precision
If high quantization precision is used, then distortion characteristics are improved, but bitrate increases and power consumption increases
Solution Approach 1:
The system applies different quantization precisions to different blocks based on their content categories. Blocks with more important visual content (e.g., faces, text) receive higher precision, while less important areas (e.g., uniform skies, water) receive lower precision. This localized approach improves overall distortion characteristics while minimizing unnecessary power consumption associated with high-precision processing throughout the entire image.
Solution Approach 2:
The quantization parameter is dynamically changed based on the content category of each block. By adjusting the quantization parameter according to block content (e.g., using lower QP values for important content and higher QP values for less important content), the system optimizes the balance between distortion characteristics and power consumption without requiring uniformly high precision across the entire video frame.
Data Source
AI summary
In some embodiments, encoding modes for a video image block are enabled according to similarity measures of the block with respect to multiple content categories. Content categories may identify potentially overlapping content types such as sky, water, grass, skin, and red content. In a priority mode, the encoding modes specified by a priority content category (e.g. a red category) are selectively enabled for the block, regardless of the block's similarity to other (non-priority) content categories, provided the block is sufficiently similar to the priority category. In a dominant mode, the encoding modes enabled by a maximum-similarity content category are enabled for the block. In an all-inclusive mode, any mode enabled by any sufficiently-similar content category is enabled for the block. Enabled encoding modes may be further evaluated for selection for the block. Encoding modes may include inter/intra modes, macroblock partition sizes, and intra-prediction directions.