Content-Adaptive Quantization Kernel for Video Encoding Rate-Distortion
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Solution Overview
Problem
Existing video encoding technologies face inefficiencies in rate-distortion performance due to constant quantization parameter selection, which does not adapt to the characteristics of individual video frames, leading to suboptimal compression and transmission bandwidth usage.
Innovation Solution
The content-adaptive selection of quantization parameters is achieved by mapping block model parameters to quantization parameters, using a Lagrange multiplier to determine suitable parameters for each frame, computed offline to reduce real-time encoding calculations and improve rate-distortion performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If constant quantization parameter is used throughout video coding, then device complexity is reduced, but rate-distortion performance deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static constant quantization parameters to dynamic content-adaptive quantization parameters. The system calculates quantization parameters based on frame characteristics (motion vectors, quantization step sizes) for each frame or group of frames, allowing the quantization strategy to adapt to varying video content complexity and achieve optimal rate-distortion performance.
Solution Approach 2:
The patent implements parameter changes by modifying quantization parameters based on calculated frame characteristics. The system computes new quantization parameters (Qp, Qs, Qd) using formulas that incorporate motion vector magnitude and previous frame quantization settings, enabling continuous adaptation to content changes while maintaining manageable computational complexity through incremental updates.
2Reliability
If content-adaptive quantization parameters are selected for each frame, then rate-distortion performance is improved, but computational load increases
Solution Approach 1:
The patent applies partial action by calculating quantization parameters selectively based on frame characteristics rather than processing every pixel or coefficient individually. The system computes aggregate frame metrics (motion vector statistics, quantization step sizes) and derives quantization parameters from these summaries, achieving content-adaptivity without the excessive computational cost of per-element optimization.
Solution Approach 2:
The patent implements preliminary action by pre-calculating and storing quantization parameter mappings in lookup tables during an offline phase. During actual encoding, the system queries these pre-computed tables using frame characteristics as indices, significantly reducing real-time computational load while maintaining content-adaptive performance benefits.
Data Source
AI summary
An offline quantization module is used to optimize a rate-distortion task. The offline quantization module calculates a quantization kernel for a range of computable block parameters and a range of rate-distortion slope values representing the rate and complexity of a coded video. A quantization kernel is utilized by an encoder application for content-adaptive quantization of transformed coefficients. The quantization kernel includes a block data model, a quality metric model, and an entropy coding model. The quantization kernel is suitable for existing and future coding standards. A rate-distortion slope selection process is performed on a per-frame basis for improved rate-distortion performance. The slope is selected by referring to the block model parameter value within the quantization kernel.