Vector Spatio-Temporal Blue Noise Masks for Image Rendering
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Solution Overview
Problem
Conventional image rendering techniques that do not consider the temporal aspect of noise mask generation produce images that are less visually pleasing, as they fail to effectively animate blue noise masks over time, leading to a tradeoff between spatial and temporal quality.
Innovation Solution
The development of vector spatio-temporal blue noise masks, which store vector values per pixel and are optimally blue over both space and time, using a modified blue noise dithered sampling algorithm to generate noise patterns that solve constraints simultaneously for real-time image rendering, incorporating a time axis to improve image quality in motion and applications like temporal anti-aliasing and deep learning super sampling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image rendering techniques are used without temporal consideration in noise mask generation, then the processing is simpler and faster, but the image quality is less visually pleasing and temporal stability is reduced
Solution Approach 1:
The patent extends the noise mask generation from 2D spatial domain to 3D spatio-temporal domain by adding the time dimension. This is achieved by generating a sequence of blue noise masks over time rather than using static masks, thereby improving temporal stability and visual quality while maintaining manageable processing complexity through efficient algorithms.
Solution Approach 2:
The patent introduces temporal dynamics to the noise mask generation process by creating animated blue noise masks that evolve over time. The masks are generated with temporal coherence, ensuring smooth transitions between frames while maintaining blue noise properties, thus improving visual quality without excessive computational burden.
2Manufacturing precision
If blue noise masks are animated over time to improve temporal quality, then image quality in motion is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent pre-generates blue noise masks for multiple time frames in advance and stores them in a lookup table or buffer. During real-time rendering, these pre-computed masks are retrieved and applied, significantly reducing the processing time required during actual image generation while maintaining high temporal quality.
Solution Approach 2:
The patent generates blue noise masks periodically at predetermined time intervals rather than continuously. This periodic generation approach reduces computational overhead by creating masks only when necessary, while still providing sufficient temporal sampling to maintain image quality in motion sequences.
3Measurement precision
If vector values are stored per pixel in spatio-temporal masks, then convergence rates improve and error patterns are more stable, but the memory requirements increase
Solution Approach 1:
The patent applies different data representations to different regions or contexts within the noise mask system. Vector values are used where high precision is needed (such as in regions with complex lighting or motion), while scalar values or reduced precision representations are used in other areas, thereby optimizing the balance between convergence rate and memory usage.
Data Source
AI summary
Apparatuses, systems, and techniques to generate blue noise masks for real-time image rendering and enhancement. In at least one embodiment, a vector-valued noise mask is generated and applied to one or more images to generate one or more enhanced images for image processing (e.g., real-time image rendering). In at least one embodiment, the noise mask includes vector values per pixel and is able to handle the temporal domain (e.g., add time to the spatial domain) to improve image quality when rendering images over multiple frames.