Adaptive Video Encoder Control for Complexity-Rate-Quality Trade-offs
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Solution Overview
Problem
High-efficiency video coding (HEVC) standards face challenges in jointly controlling bitrate, video quality, and computational complexity for both intra-coding and inter-coding, as existing methods do not adequately consider varying network conditions, energy/power constraints, and video quality expectations.
Innovation Solution
The development of adaptive methods that dynamically adjust video compression parameters, such as CTU configurations and quantization parameters, to minimize computational complexity while maximizing image quality and minimizing bandwidth, using a controller to optimize encoding configurations and apply Pareto front optimization techniques for both intra-coding and inter-coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If HEVC uses larger coding tree unit (CTU) sizes to increase coding efficiency, then compression efficiency is improved and decoding time is reduced, but computational complexity for encoding increases
Solution Approach 1:
The patent implements dynamic CTU size selection where the encoder adapts the CTU size based on real-time analysis of video content characteristics and constraints. The system evaluates multiple CTU size options and selects the optimal size for each coding unit, allowing the system to switch between different coding efficiencies depending on the specific requirements of the video data and available computational resources.
Solution Approach 2:
The patent changes the parameter of CTU size dynamically during the encoding process. By varying the CTU size parameter based on content complexity, scene changes, and computational constraints, the system achieves a balance between compression efficiency and computational complexity. The encoder adjusts this parameter to optimize performance under different operating conditions.
2Productivity
If HEVC uses complex intra-prediction modes and asymmetric inter prediction unit division, then coding efficiency is improved, but encoding time and computational complexity increase
Solution Approach 1:
The patent applies partial prediction modes selectively rather than exhaustively testing all possible prediction modes. The system evaluates a subset of most likely prediction modes first and only performs full complex prediction when necessary, based on preliminary assessments of video content characteristics and constraints. This partial action approach reduces encoding time while maintaining high coding efficiency for the majority of cases.
Solution Approach 2:
The patent performs preliminary analysis of video content to predict which prediction modes will be most effective before fully executing the encoding process. By assessing content characteristics in advance and pre-determining optimal prediction strategies, the system avoids unnecessary computational attempts at complex predictions, thereby reducing overall encoding time while preserving coding efficiency.
3Productivity
If video compression requirements are reduced to lower computational complexity, then encoding speed is improved, but bitrate and video quality deteriorate
Solution Approach 1:
The patent applies different coding strategies to different regions of the video content based on their specific characteristics. High-quality regions with important visual information receive more sophisticated prediction and coding treatment, while lower-quality or less important regions use simpler, faster encoding methods. This local differentiation allows the system to maintain video quality where needed while achieving high encoding speeds overall.
Solution Approach 2:
The system dynamically adjusts the balance between encoding speed and quality by continuously monitoring constraints such as available computational resources, time limits, and quality requirements. The encoder adapts its complexity level in real-time, switching between high-quality and speed-optimized modes based on current operational conditions, thereby achieving the optimal balance between encoding speed and video quality for each specific task.
4Loss of time
If adaptive methods dynamically adjust compression parameters to minimize computational complexity, then encoding time is reduced, but control precision over quality and rate requires sophisticated optimization
Solution Approach 1:
The patent implements feedback mechanisms where the encoder continuously monitors encoding performance, quality metrics, and computational resource consumption. Based on this feedback, the system automatically adjusts compression parameters such as CTU size, prediction modes, and quantization levels. The feedback loop enables the system to learn from previous encoding attempts and refine its parameter selection, reducing the need for complex manual control while maintaining optimal performance.
Solution Approach 2:
The encoding system performs self-optimization by automatically selecting compression parameters based on its own performance metrics and constraints. Rather than requiring external control, the system monitors its own encoding time, quality output, and computational usage, and autonomously adjusts its parameters to achieve optimal balance. This self-service capability simplifies the overall control architecture while maintaining sophisticated optimization of encoding time and quality.
Data Source
AI summary
System and methods for the joint control of reconstructed video quality, computational complexity and compression rate for intra-mode and inter-mode video encoding in HEVC. The invention provides effective methods for (i) generating a Pareto front for intra-coding by varying CTU parameters and the QP, (ii) generating a Pareto front for inter-coding by varying GOP configurations and the QP, (iii) real-time and offline Pareto model front estimation using regression methods, (iv) determining the optimal encoding configurations based on the Pareto model by root finding and local search, and (v) robust adaptation of the constraints and model updates at both the CTU and GOP levels.