Tone Mapping Using Sparse Image Data Sampling
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Solution Overview
Problem
Existing tone mapping techniques for image streams face challenges in minimizing image artefacts and maintaining continuous processing, especially when image content changes significantly between consecutive images, leading to sub-optimal display of highlights and shadows.
Innovation Solution
A method that involves sparsely reading image data from strategically distributed positions in an image, generating tone mapping parameters based on this sparse data, and applying them to both sparse and full positions within zones, allowing for efficient tone mapping while reducing processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If local tone mapping parameters are extracted from one image and applied to the subsequent image, then processing requirements are reduced, but image artefacts are introduced particularly when image content changes significantly
Solution Approach 1:
The patent extracts local tone mapping parameters from a first image in advance before processing the second image. This preliminary extraction of parameters (such as local gamma values, contrast parameters, or tone curve data) allows the second image to be processed more efficiently while maintaining quality, as the parameters are prepared beforehand rather than computed in real-time during image processing
Solution Approach 2:
The patent applies different tone mapping parameters to different local regions or zones of the image rather than using a single global parameter set. This local quality approach ensures that each region is processed with parameters optimized for its specific characteristics, reducing artefacts while maintaining processing efficiency through the use of pre-extracted regional parameters
2Device complexity
If global tone mapping algorithms are used, then processing is simplified, but contrast reproduction deteriorates resulting in sub-optimal display of highlights and shadows
Solution Approach 1:
The patent divides the image into multiple local regions or zones and extracts tone mapping parameters for each region separately. This segmentation allows the system to capture local contrast variations throughout the image, improving highlight and shadow reproduction while keeping the overall algorithm manageable through systematic regional processing rather than requiring complex global analysis
3Manufacturing precision
If local tone mapping algorithms are used, then image quality is improved, but processing requirements increase
Solution Approach 1:
The patent performs the computationally intensive local parameter extraction from the first image in advance, storing these parameters for reuse. This preliminary action shifts the processing burden to a pre-processing stage, allowing the actual processing of the second image to be much faster while still benefiting from the quality improvements of local tone mapping
Solution Approach 2:
The patent extracts specific tone mapping parameters (such as local gamma, contrast, or histogram data) from the first image and applies them to the second image. By changing and reusing these parameters across multiple images, the system achieves high-quality local tone mapping without repeatedly performing full local analysis, thus improving processing efficiency
Data Source
AI summary
A method of performing tone mapping in a stream of images (Fr1 . . . N) includes, for each image (FrN) in the stream: sparsely reading image data values (IDN) corresponding to the image (FrN) to provide sparse image data from a plurality of sparsely distributed positions (Pos1 . . . k) in the image (FrN); generating, based on the sparse image data, tone mapping parameters of a tone mapping algorithm (TMA) for each position in the image (FrN); each position in the image (FrN) including the sparsely distributed positions (Pos1 . . . k) and a plurality of further positions (PosF1 . . . j) in the image (FrN); reading the image data values (IDN) corresponding to the image (FrN) to provide image data from each position in the image (FrN); and tone mapping the image by mapping the image data from each position in the image (FrN) to adjusted image data using the generated tone mapping parameters.