Probe Data Normalization for Low-Loss Rendering Compression
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Solution Overview
Problem
The compression of probe data in rendering systems causes significant rendering loss, necessitating a solution to minimize this loss while maintaining image quality.
Innovation Solution
An encoding method that determines a target normalization combination to minimize rendering loss by selecting the optimal normalization method and parameter, followed by encoding the normalized probe data into a bitstream, and a decoding method that utilizes the same combination to denormalize the data for rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If probe data is compressed to reduce storage and transmission overheads, then storage and transmission efficiency is improved, but rendering loss increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting normalization parameters (such as normalization methods and scaling factors) based on the characteristics of probe data. Instead of using fixed compression parameters, the system selects optimal normalization parameters adaptively to minimize rendering loss while achieving compression, directly resolving the contradiction between compression efficiency and rendering quality.
Solution Approach 2:
The patent implements dynamics by transitioning from static, fixed normalization parameters to dynamic, adaptive normalization parameters that change based on probe data characteristics. The system continuously adjusts normalization parameters during encoding to optimize the balance between compression ratio and rendering accuracy, allowing the compression process to adapt to varying data conditions.
2Ease of manufacture
If a fixed normalization method is used for encoding probe data, then encoding simplicity is improved, but rendering accuracy deteriorates
Solution Approach 1:
The patent replaces static normalization methods with dynamic normalization methods that automatically adapt to different probe data characteristics. The system selects and applies different normalization parameters based on the specific properties of the data being encoded, maintaining encoding simplicity through automation while significantly improving rendering accuracy through adaptivity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the encoding system evaluates the characteristics of probe data and adjusts normalization parameters accordingly. This feedback loop allows the system to automatically optimize normalization parameters based on actual data properties, improving rendering accuracy without requiring complex manual configuration or sacrificing encoding simplicity.
Data Source
AI summary
Embodiments of the present disclosure relate to the field of media technologies, and disclose an encoding method and apparatus, and a decoding method and apparatus, to reduce a rendering loss caused by compression of probe data. The encoding method includes: first determining a target normalization combination of a probe data group, then normalizing the probe data group based on the target normalization combination to obtain a normalized probe data group, and encoding the normalized probe data group into a bitstream. The target normalization combination minimizes a rendering loss corresponding to the probe data group among a plurality of normalization combinations, and the target normalization combination includes a target normalization method and a target normalization parameter.