Video Encoder Quantization Control Spatial Portions
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Solution Overview
Problem
Existing video encoding technologies apply uniform quantization to all images or portions, which may not be suitable for varying characteristics and environments, leading to suboptimal encoding performance.
Innovation Solution
Implementing different quantization matrices and scaling parameters for different spatial frequencies and portions of images based on characteristics such as signal-to-noise ratio, target bit rate, and channel conditions to tailor quantization to specific image and transmission environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform quantization is applied to all images or portions, then the encoding process is simple and fast, but the encoding performance becomes suboptimal for varying characteristics and environments
Solution Approach 1:
The patent divides the image into multiple spatial portions (e.g., slices, blocks) and applies different quantization matrices to each portion based on its characteristics. This segmentation allows optimized quantization for each region while maintaining overall system manageability.
Solution Approach 2:
The patent applies different quantization parameters and matrices to different spatial portions of the image based on local characteristics such as signal-to-noise ratio, texture complexity, and frequency content. This local optimization improves encoding performance without requiring complete redesign of the entire quantization system.
2Productivity
If different quantization matrices are applied to different spatial frequencies and portions, then encoding efficiency and visual quality improve, but the complexity of the encoding process increases
Solution Approach 1:
The patent dynamically selects quantization matrices based on the characteristics of each spatial portion, such as signal-to-noise ratio and frequency content. The system adapts the quantization strategy in real-time based on actual image data rather than using a fixed predetermined matrix for all cases.
Solution Approach 2:
The patent changes quantization parameters (such as quantization step size and matrix coefficients) based on the specific characteristics of each spatial portion. By adjusting these parameters adaptively, the system optimizes encoding efficiency while managing complexity through parameterization rather than complete system redesign.
3Manufacturing precision
If quantization is optimized for specific image characteristics and transmission conditions, then bit rate management and visual quality improve, but the adaptability requirements and system complexity increase
Solution Approach 1:
The patent incorporates feedback mechanisms that analyze image characteristics and transmission conditions to adjust quantization parameters accordingly. The system uses feedback from quality assessment and bit rate monitoring to dynamically optimize quantization for each spatial portion based on actual performance data.
Solution Approach 2:
The patent performs preliminary analysis of image characteristics and transmission conditions before applying quantization. By assessing factors such as signal-to-noise ratio, frequency content, and channel conditions in advance, the system pre-determines optimal quantization matrices and parameters to avoid complex real-time adjustments.
Data Source
AI summary
In one implementation, a method of encoding an image is performed at a device including one or more processors and non-transitory memory. The method includes determining a category of a spatial portion of an image based on a relation between a plurality of thresholds associated with a plurality of quantization scaling parameters and a bit rate of the spatial portion of the image at the plurality of quantization scaling parameters. The method includes quantizing the spatial portion of the image based on the categorization.