Video Signal Grading via Histogram Correction
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Solution Overview
Problem
Existing video signal processing methods fail to provide appropriate gradation correction, leading to improper adjustments in gamma ramps, which result in lower contrast and image quality issues due to emphasis on false edges and noise components, especially in dark zones and high luminance sections.
Innovation Solution
A method and apparatus that extract low and high-frequency components from video signals, produce luminance and high-frequency component histograms, and correct the luminance histogram data to generate a gradation correction curve, ensuring appropriate gradation distribution and contrast across different image zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If gamma correction is applied to video signals to adjust gamma curves for image-quality improvement, then image quality is improved, but false edges or noise components are emphasized in dark zones, and black floating occurs, leading to lowered contrast and image quality
Solution Approach 1:
The patent divides the image into multiple zones based on luminance levels (dark zone, intermediate zone, bright zone) and applies different gamma ramp characteristics to each zone. This segmentation allows the system to avoid applying steep gamma ramps to dark zones with large areas, thereby preventing false edge emphasis and noise component generation while still achieving appropriate gradation correction in each zone
Solution Approach 2:
The patent applies different gamma correction characteristics to different spatial regions of the image based on their luminance distribution. Specifically, dark zones occupying large areas receive gentler gamma ramps to prevent black floating and noise emphasis, while other zones receive appropriate gamma correction for their characteristics, achieving localized optimization of image quality
2Manufacturing precision
If a steeper gamma ramp is applied to high luminance-level sections (such as sky) that occupy large zones, then gradation adjustment is achieved, but objects at intermediate luminance-level sections suffer gentler gamma ramps, resulting in lower contrast and dark impression
Solution Approach 1:
The patent segments the luminance range into multiple zones (dark, intermediate, bright) and applies differentiated gamma ramp characteristics to each. High luminance-level sections receive appropriate gamma correction without excessively steep ramps, while intermediate luminance sections maintain sufficient contrast through tailored gamma characteristics, preventing the dark impression problem
Solution Approach 2:
The system applies locally optimized gamma correction where intermediate luminance sections (containing objects like buildings and human beings) receive gamma ramps designed to preserve contrast, while high luminance sections (like sky) receive gentler correction appropriate to their characteristics, achieving balanced local quality across the entire image
Data Source
AI summary
Low frequency components are extracted from a video signal for pixels in a specific area of an image carried by the signal. The low frequency components are allocated, based on luminance components of the signal, to gradations ranging from maximum to minimum levels exhibited by the luminance components, luminance histogram data being produced indicating the frequency of gradations for the pixels. The gradations are allocated to the pixels based on the luminance components to produce high-frequency component histogram data indicating the frequency of gradations for specific pixels each carrying a high frequency component. The luminance histogram data is corrected in accordance with the frequency of gradations in the high-frequency component histogram data to produce corrected luminance histogram data. A gradation correction curve is produced for correcting the gradations exhibited by the signal using the corrected data. The luminance components are corrected based on the correction curve.