Integrated Circuit Nonlinear Data Encoding for HDR Displays
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Solution Overview
Problem
Conventional methods for nonlinear encoding in display devices, particularly for high dynamic range (HDR) imaging, face challenges in efficiently processing and storing Electro-Optical Transfer Functions (EOTFs) due to high memory requirements and impracticality of using lookup tables (LUTs), leading to difficulties in maintaining image quality and precision.
Innovation Solution
An integrated circuit is used to perform nonlinear encoding of linear image data through piecewise quantization and compressive addressing, allowing for efficient memory usage by generating a memory address based on the quantized data and retrieving output values from a compact lookup table, reducing redundancy and memory size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional lookup tables are used to store pre-computed EOTF functions, then nonlinear encoding can be achieved, but memory requirements become impractically large
Solution Approach 1:
The patent divides the lookup table into multiple segments or blocks, each storing EOTF values for a specific range of input values. Instead of storing the entire EOTF curve in one large table, the function is segmented across multiple smaller tables that can be accessed individually based on the input value range, thereby reducing the memory burden of any single table while maintaining complete EOTF coverage.
Solution Approach 2:
The patent introduces an additional addressing dimension by using a two-stage addressing mechanism: first determining which segment block to access based on the input value range, then determining the specific offset within that block. This dimensional approach to memory addressing allows efficient access to EOTF values without requiring a single large contiguous memory space.
2Measurement precision
If high precision linear image data is processed, then image quality is maintained, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into two distinct stages: first determining the appropriate segment block based on the input value range, then performing the actual EOTF calculation within that block. This segmentation of computation reduces the complexity of any single computational step while maintaining overall high precision through the multi-stage approach.
3Quantity of substance
If piecewise quantization is applied to reduce memory requirements, then memory efficiency improves, but encoding accuracy may be compromised
Solution Approach 1:
The patent applies different quantization characteristics to different segments of the EOTF curve, allowing each segment to be optimized for its specific range. This local quality approach ensures that quantization accuracy is maintained where it matters most (in darker regions where human vision is more sensitive) while allowing coarser quantization in brighter regions, thereby balancing memory efficiency with encoding accuracy across the entire dynamic range.
Data Source
AI summary
A method of image processing, the method including performing linear processing of an input data signal encoded with a nonlinear function to generate a linear representation of the input data signal including linearized image data, and using an integrated circuit to generate a processed linear image by nonlinearly quantizing the linearized image data to generate nonlinear quantized data, generating a memory address based on the nonlinear quantized data, and accessing a lookup table based on the generated memory address.