Luminance-Based Video Coding for HDR Artifact Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional video coding standards, such as HEVC, struggle with preserving visual quality when encoding and decoding high dynamic range (HDR) images due to limited bit depths and lossy compression, leading to noticeable visual artifacts in bright and dark areas on modern displays.
Innovation Solution
The implementation of luminance-dependent signal processing operations in video encoders and decoders, which adapt techniques like non-linear mappings, internal precision adjustments, and region-specific filtering to minimize errors and optimize bit-depth usage across varying luminance levels, allowing for enhanced dynamic range and color gamut support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional video coding standards (HEVC, H.264/AVC) are used to encode HDR images, then compression efficiency is maintained, but visual quality deteriorates in bright and dark areas due to limited bit depths and lossy compression
Solution Approach 1:
The patent applies different signal processing operations to different luminance regions (bright areas, mid-tone areas, dark areas) based on detected luminance levels. This local quality approach allows optimization of coding precision where it matters most - in highlight and shadow regions that are most susceptible to visual artifacts - while maintaining acceptable compression elsewhere.
Solution Approach 2:
The patent dynamically adjusts coding parameters such as bit depth, quantization step sizes, and signal processing operation selection based on the luminance characteristics of different image regions. By changing these parameters adaptively according to local luminance conditions, the system preserves visual quality in critical regions while maintaining overall compression efficiency.
2Quantity of substance
If post-production images are compressed to reduce data size for transmission and storage, then bandwidth and storage requirements are reduced, but coding errors increase significantly compared to scene-referred HDR images
Solution Approach 1:
The patent employs adaptive parameter adjustment where coding precision, bit depth, and signal processing complexity are dynamically modified based on luminance level detection. In regions with extreme luminance values where visual artifacts are most noticeable, the system increases coding precision and applies specialized processing, while using more aggressive compression in mid-tone regions where human visual sensitivity is lower.
Solution Approach 2:
Different compression strategies are applied to different spatial regions of the image based on their luminance characteristics. Bright and dark regions receive enhanced processing and higher effective bit depth to preserve detail, while mid-tone regions use standard compression, achieving an optimal balance between overall data size and localized visual quality.
3Manufacturing precision
If video coding standards are enhanced to support higher dynamic range and wider color gamut, then rendering quality on modern displays improves, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the luminance range into distinct regions (bright areas, mid-tone areas, dark areas) and applies different signal processing operations to each segment. This segmentation approach allows the system to handle HDR content with varying luminance characteristics using targeted, simpler processing rules for each region rather than requiring a completely new complex coding standard for all scenarios.
Solution Approach 2:
The patent introduces dynamic adaptation where the video coding system adjusts its processing behavior based on the actual luminance characteristics of the input content. Luminance level detection triggers selective application of different signal processing operations, allowing the system to optimize for HDR content when needed while maintaining compatibility with standard content, thereby managing complexity through conditional rather than universal enhancement.
Data Source
AI summary
Sample data and metadata related to spatial regions in images may be received from a coded video signal. It is determined whether specific spatial regions in the images correspond to a specific region of luminance levels. In response to determining the specific spatial regions correspond to the specific region of luminance levels, signal processing and video compression operations are performed on sets of samples in the specific spatial regions. The signal processing and video compression operations are at least partially dependent on the specific region of luminance levels.