Pixel Luminance Reduction Using Visual Sensitivity Thresholds
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Solution Overview
Problem
Display devices, particularly those based on OLED technology and back-lit LCDs, consume significant energy due to linear relationships between light emission and energy use, exacerbated by increasing resolutions and dynamic range imaging, posing challenges for energy efficiency and battery life.
Innovation Solution
A method to reduce pixel values by determining minimum detectable modulation using human visual sensitivity models, such as Barten's CSF, and applying continuous or discrete wavelet transforms to adjust luminance levels below perceptual thresholds, ensuring energy reduction without visible degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If pixel luminance is reduced to decrease energy consumption, then energy use is improved, but visual quality deteriorates
Solution Approach 1:
The patent applies different luminance reduction strategies to different spatial regions and frequency components of the image. Wavelet transform decomposes the image into multiple frequency bands, and luminance reduction is applied selectively to high-frequency components while preserving low-frequency components that carry essential visual information. This local differentiation allows energy reduction without uniformly degrading visual quality across the entire image.
Solution Approach 2:
The patent dynamically adjusts luminance values based on the local characteristics of each image region. By analyzing the frequency content and applying adaptive luminance reduction thresholds, the system optimizes the balance between energy consumption and visual quality on a per-region basis, rather than applying a static global reduction.
2Reliability
If display resolution is increased to improve image quality, then visual quality is improved, but energy consumption increases
Solution Approach 1:
The patent segments the image into frequency components using wavelet transform, separating the image into approximation coefficients (low-frequency) and detail coefficients (high-frequency). This segmentation allows selective processing where energy-intensive high-frequency components are reduced while preserving the essential low-frequency structure, thereby maintaining perceived image quality at lower energy consumption.
Solution Approach 2:
The patent changes the luminance parameter selectively based on frequency content. By transforming the image to the frequency domain and applying luminance reduction only to specific frequency bands, the system optimizes the luminance parameter distribution to reduce overall energy consumption while preserving visual quality through careful parameter selection and transformation.
3Use of energy by moving object
If luminance reduction is applied to save energy, then energy consumption is reduced, but detectability of image details deteriorates
Solution Approach 1:
The patent dynamically determines luminance reduction thresholds based on the local frequency content and visual importance of different image regions. By adaptively adjusting the reduction amount according to local characteristics, the system maintains detectability of important details while maximizing energy savings in less critical regions.
Solution Approach 2:
The patent applies different luminance reduction levels to different local regions based on their frequency content and visual importance. High-frequency regions that contain critical detail information receive different treatment compared to low-frequency regions, ensuring that detectability is maintained where needed while energy consumption is reduced where possible.
Data Source
AI summary
A method and device for reducing power consumption of display devices proposes to reduce the total amount of light emitted in a perceptually indistinguishable manner based on the on the human visual sensitivity at the position of a pixel. The visibility of the processing can be made to remain provably below threshold, whereas other techniques may not be able to provide such proof. This is achieved by determining a minimum detectable modulation or a corresponding just-noticeable difference based on frequency intensity information for a pixel representative of how much there is of a set of frequencies at the pixel and a contrast sensitivity function representative of a model of human vision that predicts which contrasts at which frequencies are visible to the human eye. Pixel values may be reduced based on this minimum detectable modulation or just-noticeable difference. The contrast sensitivity function may be given by Barten's model and the frequency intensity information may be based on a hierarchical map built using a discrete wavelet transform or a continuous wavelet transform.