Banding Artifact Detection With Local Dithering for Video Frames
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Solution Overview
Problem
Banding artifacts, characterized by stairstep-like contours, are introduced during the processing of smooth color transitions in video frames, often exacerbated by encoding and noticeable on electronic displays, making detection and removal difficult due to noise in smooth regions.
Innovation Solution
A method involving spatial masking techniques to identify pixels likely to cause banding, computing local distributions for affected regions, and applying dithering to detected bands to smooth out artifacts, using pseudorandom noise generation based on pixel statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video frames undergo compression and encoding for transmission, then the video can be transmitted over the internet efficiently, but banding artifacts are introduced during these processes
Solution Approach 1:
The system performs preliminary detection of banding artifacts in the video frame before output, using spatial mask generation and local distribution analysis to identify affected regions. This allows the system to proactively prepare for artifact removal by calculating dithering parameters in advance, rather than reacting after the artifacts have been displayed.
Solution Approach 2:
The system converts the harmful banding artifacts into a benefit by using dithering techniques. The dithering process introduces controlled noise that breaks up the visible banding patterns, transforming the harmful compression artifacts into an acceptable visual output that appears more natural to human observers.
2Manufacturing precision
If dithering is applied to remove banding artifacts, then video quality is improved, but the processing complexity increases
Solution Approach 1:
The system segments the video frame processing into distinct stages: spatial mask generation, local distribution computation, band detection, and dithering application. By dividing the complex task of banding removal into these manageable segments, the system can apply appropriate algorithms to each stage without overwhelming computational complexity.
Solution Approach 2:
The system applies dithering locally to only those regions of the video frame that contain banding artifacts, rather than processing the entire frame uniformly. The spatial mask identifies specific pixel regions affected by banding, allowing the dithering operation to be concentrated where it is most needed, thus improving video quality while minimizing unnecessary processing.
3Measurement precision
If spatial masking is used to identify pixels likely to cause banding, then detection accuracy is improved, but the computational load increases
Solution Approach 1:
The system performs partial action by generating spatial masks that identify only the most likely candidates for banding artifacts, rather than analyzing every pixel in the frame with equal depth. The local distribution analysis focuses computational resources on regions identified by the spatial mask, applying excessive analysis only where needed to achieve sufficient detection accuracy.
Data Source
AI summary
A computer-implemented method includes accessing a video frame that includes a plurality of pixels. The method also includes generating a spatial mask that identifies pixels whose likelihood of causing banding within the video frame meets at least a minimum threshold likelihood. The method also includes computing a local distribution for a region of the video frame that includes at least those pixels that were identified by the spatial mask to detect bands in the video frame. The method further includes determining that the detected bands in the video frame are at least a minimum threshold size. The method also includes applying dithering to those detected bands in the video frame that were determined to meet the minimum threshold size. Various other methods, systems, and computer-readable media are also disclosed.