Adaptive Sampling Quantization for Image Quality and Frame Rate
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional adaptive sampling techniques for interactive graphics systems result in a 'staircased' distribution of samples, failing to achieve desired image quality due to rounding or truncation of importance values, leading to suboptimal frame rates and power usage.
Innovation Solution
The method involves determining a target per-pixel sampling rate, distributing samples based on an importance map, and quantizing these samples using random values to ensure a smoother distribution that matches the importance map, thereby optimizing frame rate and power efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional adaptive sampling techniques use rounding or truncation to determine integer samples per pixel, then the sampling distribution becomes staircased, but the image quality deteriorates and fails to match the importance map
Solution Approach 1:
The patent changes the quantization parameter from deterministic (rounding/truncation) to stochastic (random rounding based on fractional parts). This allows the sample distribution to smoothly transition between integer values while maintaining the importance map's proportional relationships, resolving the contradiction between operational simplicity and image quality precision
Solution Approach 2:
The patent introduces dynamic randomness into the sample allocation process. Instead of static rounding rules, the system dynamically adjusts sample counts based on per-pixel random values, creating a flexible distribution that adapts to maintain both the fixed budget constraint and the importance map proportions, thereby improving image quality without sacrificing operational simplicity
2Manufacturing precision
If more samples are distributed to all pixels to improve image quality, then image quality improves, but the frame rate deteriorates due to increased computational effort
Solution Approach 1:
The patent applies local quality by distributing samples non-uniformly across the image based on an importance map. High-importance pixels receive more samples while low-importance pixels receive fewer samples, maintaining overall image quality while reducing the total sample count compared to uniform sampling, thereby preserving frame rate
Solution Approach 2:
The patent uses partial action by allocating samples only where necessary according to the importance map. Instead of uniformly increasing samples across all pixels, the system applies sampling effort selectively to regions that benefit most, achieving acceptable image quality with reduced computational effort and maintained frame rate
3Use of energy by moving object
If a fixed budget of samples is used for the entire image, then power consumption is reduced, but the image quality deteriorates due to insufficient samples in important regions
Solution Approach 1:
The patent changes the sample distribution parameter from uniform to importance-weighted. By modifying how the fixed budget is allocated across pixels based on importance values, the system maintains power efficiency while ensuring critical regions receive adequate sampling, resolving the contradiction between energy usage and image quality
Solution Approach 2:
The patent applies partial action by concentrating sampling resources on important regions rather than distributing them uniformly. This selective approach ensures that the fixed budget adequately samples critical areas while using fewer samples in less important areas, maintaining image quality within the energy constraints of the fixed budget
4Manufacturing precision
If the number of samples per pixel is increased uniformly, then image quality improves, but the computational effort increases and frame rate deteriorates
Solution Approach 1:
The patent applies local quality by varying the sample count per pixel based on local importance values. Instead of uniformly increasing samples across the entire image, the system selectively increases samples only in regions where it matters, reducing total computational time while maintaining image quality
Solution Approach 2:
The patent uses partial action by applying increased sampling only where necessary according to the importance map. This selective approach achieves the desired image quality in critical regions without the computational overhead of uniformly increasing samples across all pixels, thereby reducing overall computational time
Data Source
AI summary
Image quality can be improved by rendering the image based on an importance map that indicates regions of the image that will benefit from more samples. Adaptive sampling determines a number of samples for each pixel of the image using a target sampling rate and the importance map for the image. The number of samples for each pixel needs to be a non-negative integer value, so the per-pixel sampling rates are quantized using per-pixel random values. The resulting quantized sampling rates provides a distribution of samples that closely matches the importance map. The per-pixel random values may vary over time so that the average of the distribution more closely matches the importance map.