SDR to HDR Conversion Noise Reduction via Temporal Histogram Variance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Noise artifacts in high-dynamic range (HDR) images reconstructed from standard-dynamic range (SDR) images become enhanced and are deemed unacceptable during SDR to HDR conversion, leading to visually annoying issues and low-quality encoding or display.
Innovation Solution
A processor-based system that estimates noise artifacts by calculating the temporal histogram variance of SDR images and adjusts the backward reshaping function to reduce noise in HDR images, using a sliding window and low-pass filtering to smooth variance measurements and scale noise likelihood based on the first derivative of the reshaping function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If standard SDR to HDR conversion is applied, then dynamic range is improved, but noise artifacts are enhanced and become unacceptable
Solution Approach 1:
The patent applies preliminary denoising actions before the SDR to HDR conversion process. By analyzing temporal histograms of SDR frames and identifying noise characteristics in advance, the system pre-adjusts the backward reshaping function to prevent noise amplification during conversion, rather than dealing with noise artifacts after they are enhanced.
Solution Approach 2:
The patent modifies the backward reshaping function parameters based on temporal histogram variance analysis. By dynamically adjusting the reshaping function's derivative values according to measured noise levels in the SDR sequence, the system changes the conversion parameters to minimize noise amplification while preserving legitimate image details.
2Object-affected harmful factors
If noise reduction is applied during SDR to HDR conversion, then noise artifacts are reduced, but image quality may be compromised
Solution Approach 1:
The patent applies different processing strategies to different regions and components of the image based on local noise characteristics. By analyzing temporal histograms separately for different luminance ranges and applying region-specific adjustments to the backward reshaping function, the system preserves image quality in clean regions while aggressively denoising only where noise is detected.
Solution Approach 2:
The patent employs feedback mechanisms by continuously monitoring temporal histogram variance and using this information to dynamically adjust the backward reshaping function. The system measures noise levels in the SDR sequence, feeds this information back into the conversion process, and iteratively refines the reshaping parameters to achieve optimal noise reduction while maintaining image fidelity.
Data Source
Figure 1A~1B
Figure 2
Figure 3A~3B
AI summary
Methods and systems for image denoising when displaying high-dynamic-range images are described. Given an input image in a first dynamic range, and an input backward reshaping function mapping codewords from the first dynamic range to a second dynamic range, wherein the second dynamic range is equal or higher than the first dynamic range, statistical data based on the input image and the input backward reshaping function are generated to estimate the risk of noise artifacts in a target image in the second dynamic range generated by applying the input backward reshaping function to the input image. Using a measure of the variance in codeword bins in histograms of consecutive input frames (denoted as temporal histogram variance), a modified backward reshaping function is generated, which when applied to the input image to generate the target image eliminates or reduces noise artifacts in the target image.