Cross-Display Image Enhancement for Consistent Visual Appearance
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Solution Overview
Problem
Existing image content mastering processes are costly, time-consuming, and impractical for adapting to a wide range of display platforms with varying characteristics, leading to inconsistent and compromised image quality across different display devices.
Innovation Solution
A method and system that analyze and enhance image data by comparing attributes between different display formats, using learning-based processes to generate enhanced image data that can be displayed consistently across diverse display platforms, incorporating global and local attributes through global feature transforms and local mask sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image content is professionally mastered for each display platform, then image quality consistency is improved, but cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary analysis of display platform characteristics and pre-computes enhancement parameters before actual image rendering. By analyzing display characteristics (brightness, contrast, color gamut) in advance and preparing enhancement profiles beforehand, the system avoids time-consuming adjustments during deployment while maintaining consistent image quality across different platforms
Solution Approach 2:
The system dynamically adjusts image parameters (brightness, contrast, saturation, sharpness) based on detected display platform characteristics. By automatically modifying these parameters according to the specific display device properties, the system achieves consistent visual appearance across different platforms without manual mastering for each device
2Adaptability or versatility
If image content is mastered for multiple display platforms, then adaptability is improved, but complexity of mastering process increases
Solution Approach 1:
The system creates a universal image enhancement framework that can adapt to multiple display platforms through a single automated process. By developing a multi-functional enhancement engine that handles various display characteristics (OLED, LED, LCD, different brightness levels, color gamuts) through unified algorithms, the system achieves broad platform adaptability without increasing mastering process complexity
Solution Approach 2:
The system uses reference images and enhancement profiles from one display platform to generate appropriate enhancements for other platforms. By creating master enhancement profiles that can be copied and adapted to different display types, the system reduces the complexity of creating separate mastering processes for each platform while maintaining high adaptability
3Illumination intensity
If traditional display technologies are replaced by newer generations, then visual appearance is improved, but image consistency across technologies becomes difficult to maintain
Solution Approach 1:
The system detects the display technology type and its specific characteristics (brightness capabilities, color gamut, contrast ratio) and automatically adjusts image parameters to compensate for technological differences. By dynamically changing parameters such as tone mapping curves, color space transformations, and brightness scaling based on the detected display technology, the system maintains image consistency across OLED, LED, LCD, and other display generations while preserving the visual advantages of each technology
Solution Approach 2:
The system introduces an intermediate enhancement processing layer between the source image and the display device. This intermediary process analyzes the display technology characteristics and applies appropriate transformations to bridge the gap between different display technologies, ensuring consistent image appearance while allowing each display technology to showcase its unique visual capabilities
Data Source
AI summary
A method is provided for generating enhanced image data for display on different types of display platforms. The method can include receiving a first format of display image data mastered to be displayed on a first type of display system. The method can include receiving a second format of display image data mastered to be displayed on a second type of display system that is different than the first type of display system. The method can include detecting one or more corresponding pixels in image segments among the first format of display image data and the second format of display image data. The method can include using attributes from the one or more corresponding pixel to generate enhanced image data for display.