HDR Image Luminance Thresholding via Characteristic Value
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Solution Overview
Problem
Current image processing technologies face challenges in accurately identifying luminance levels in high dynamic range (HDR) images, particularly in determining the correct luminance distribution for false color display, which affects the precision of luminance confirmation in video production.
Innovation Solution
An image processing apparatus and method that acquire luminance information and convert image data into specific sub-ranges using thresholds, where the first threshold is set based on the characteristic luminance value, allowing for accurate luminance identification and distribution confirmation through color conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual setting of color conversion thresholds is used, then flexibility in adjustment is improved, but operation complexity and time consumption increase
Solution Approach 1:
The system automatically acquires luminance information from the input HDR image data and determines color conversion thresholds without requiring manual user input. The processing unit performs self-service by autonomously analyzing the luminance distribution and setting appropriate thresholds, thereby eliminating time-consuming manual operations while maintaining operational effectiveness.
Solution Approach 2:
The system performs preliminary acquisition of luminance information from the input image data before the color conversion process. By pre-analyzing the luminance distribution and determining thresholds in advance, the system prepares the necessary parameters beforehand, streamlining the subsequent color conversion operation and reducing overall processing time.
2Productivity
If fixed color conversion thresholds are used, then processing speed is improved, but luminance identification accuracy deteriorates
Solution Approach 1:
The system employs dynamic color conversion thresholds that adapt to the specific luminance characteristics of each input HDR image. Rather than using fixed thresholds, the processing unit dynamically determines thresholds based on the acquired luminance information, allowing the system to maintain high processing speed while achieving accurate luminance identification tailored to each image's unique characteristics.
Solution Approach 2:
The system changes the threshold parameters automatically based on the luminance distribution of the input image. By adjusting the threshold values according to the specific luminance ranges present in each image, the system optimizes both processing efficiency and measurement precision, avoiding the limitations of fixed threshold approaches.
3Device complexity
If standard luminance ranges are used for color conversion, then device complexity is reduced, but adaptability to different HDR contents deteriorates
Solution Approach 1:
The system achieves universal adaptability to various HDR contents through a unified automatic threshold determination mechanism. The processing unit can handle different HDR image types and luminance distributions using the same core algorithm that acquires luminance information and dynamically sets thresholds, eliminating the need for multiple specialized configurations while maintaining broad compatibility across diverse HDR content.
Solution Approach 2:
The system performs preliminary analysis of the input HDR image's luminance characteristics before applying color conversion. This pre-processing step allows the system to adapt to different HDR contents automatically by understanding their specific luminance ranges, thereby achieving high versatility without increasing device complexity through manual configuration.
Data Source
AI summary
An image processing apparatus includes: at least one processor and/or at least one circuit to perform operations of the following units: an acquisition unit configured to acquire luminance information that indicates characteristic value of luminance of inputted image data; and a conversion unit configured to convert colors of the image data into conversion colors which are respectively associated with sub-ranges determined by dividing a luminance range of the image data using thresholds, wherein a first threshold of the thresholds is the characteristic value.