Real-Time Tone Mapping With Noise-Aware Contrast Compression
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Solution Overview
Problem
Existing tone-mapping algorithms for HDR video fail to effectively reduce noise, handle large contrast compression, and introduce artifacts such as flickering, ghosting, and ringing, while being slow to compute, making them unsuitable for real-time processing.
Innovation Solution
A real-time contrast distortion-based tone-mapping system that includes noise modeling, edge-stopping spatial filtering, and adaptive tone-curve generation to minimize image contrast distortion, incorporating contextual parameters and display adaptivity for efficient noise-aware tone-mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing tone-mapping algorithms are used, then tone-mapping function is achieved, but noise is not effectively reduced and artifacts are introduced
Solution Approach 1:
The patent segments the tone-mapping process into multiple stages: base layer extraction using edge-stopping spatial filtering, tone-curve generation based on contrast distortion models, and detail layer reconstruction. This segmentation allows each component to address specific issues independently, with the base layer handling noise reduction and the detail layer preserving edges, thereby reducing artifacts while maintaining noise reduction effectiveness.
Solution Approach 2:
The patent applies local quality by using edge-stopping spatial filtering that adapts to local image characteristics. The filtering strength and characteristics vary depending on the local content (edges vs. smooth regions), allowing optimal noise reduction in smooth areas while preserving edges and avoiding artifacts in complex regions. The tone-curve generation also adapts to local contrast distortion characteristics.
2Manufacturing precision
If existing tone-mapping algorithms are used, then tone-mapping function is achieved, but large contrast compression is not handled effectively
Solution Approach 1:
The patent employs parameter changes by dynamically generating tone-curves based on measured contrast distortion characteristics of the input image. The tone-curve parameters (gamma values, compression factors) are adjusted according to the specific contrast distribution and distortion patterns detected in the image, enabling effective handling of large contrast compression while minimizing distortion in the output. This adaptive parameter adjustment allows the system to optimize contrast compression for each specific image content.
3Productivity
If existing tone-mapping algorithms are used, then tone-mapping function is achieved, but processing speed is slow
Solution Approach 1:
The patent segments the computationally intensive tasks into independent modules that can be processed in parallel or with optimized algorithms. The base layer extraction, tone-curve generation, and detail layer reconstruction are separate stages that can be optimized independently, reducing overall computational time while maintaining quality. This segmentation enables real-time processing by identifying and optimizing the most time-consuming operations.
Solution Approach 2:
The patent applies partial action by focusing computational resources on the most critical operations. Instead of uniformly processing the entire image with high computational intensity, the system applies edge-stopping filtering and detailed tone-curve generation primarily in regions where contrast distortion and noise are most problematic, while using faster processing methods in uniform regions. This selective computation maintains quality while significantly reducing processing time.
4Manufacturing precision
If edge-stopping spatial filtering is applied, then base layer and detail layer are extracted, but computational complexity increases
Solution Approach 1:
The patent applies partial action to the edge-stopping spatial filtering by limiting the filtering operation to only those regions and scales where edge preservation is critical. The filtering strength and spatial extent are adjusted based on local image characteristics, applying complex filtering only where needed (near edges) and using simpler processing in smooth regions. This selective application maintains edge preservation accuracy while reducing overall computational complexity.
Data Source
AI summary
A method and system for tone-mapping an image includes determining a tone-curve based on a model of image contrast distortion between the input image and a tone-mapped image and tone-mapping the input image according to the determined tone-curve. Determining the tone curve includes analytically calculating values of the tone-curve that reduce the image contrast distortion within the model of image contrast distortion. A tone-mapping operator includes a noise model generator and a tone-mapping module operable to receive one or more contextual parameters. The tone-mapping module includes an edge-stopping filtering submodule for extracting a base layer and detail layer of the input image, a tone-curve generating submodule and a combining submodule for combining the base layer and detail layer. At least one of the edge-stopping filtering submodule, the tone-curve generating submodule and combining submodule is adjustable based on the contextual parameters.