Macro-Pixel Processing for Image Sensor RGB to YUV Conversion
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Solution Overview
Problem
Conventional image processing methods, particularly in devices with image sensors and displays, face inefficiencies in converting RGB images to YUV format due to the need for extensive data processing and storage, which can be improved by employing macro-pixel processing instead of raster-based processing.
Innovation Solution
The method involves determining luminance values of macro-pixels in a color space, calculating and applying gain values to pixels using a look-up table and histogram information, and processing macro-pixels rather than individual pixels to reduce computational load and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional raster-based processing is used to convert RGB images to YUV format, then image processing accuracy is maintained, but processing time and power consumption increase significantly
Solution Approach 1:
The image is divided into macro-pixels, each containing multiple individual pixels (e.g., 2x2 or 4x4 blocks). Instead of processing each pixel independently, the patent processes groups of pixels as single macro-pixels, significantly reducing the total number of processing operations required for RGB to YUV conversion while maintaining acceptable image quality.
Solution Approach 2:
Multiple individual pixels within a macro-pixel are merged into a single processing unit. The patent combines the color values of multiple pixels (e.g., R1, G1, B1, R2, G2, B2) into unified RGB macro-pixel values, which are then converted to a single YUV macro-pixel output, reducing computational complexity and processing time.
2Productivity
If conventional raster-based processing is used for RGB to YUV conversion, then processing completeness is ensured, but area usage and power consumption increase
Solution Approach 1:
The image processing is segmented into macro-pixel units, reducing the total number of conversion operations. By processing groups of pixels together rather than individually, the patent reduces the computational load and corresponding power consumption of the image processing circuitry.
Solution Approach 2:
The patent changes the processing parameter from individual pixel level to macro-pixel level. This parameter change reduces the number of arithmetic operations required for RGB to YUV conversion, thereby reducing both area usage of processing circuits and power consumption while maintaining acceptable image quality.
3Loss of time
If macro-pixel processing is implemented, then processing time and power consumption are reduced, but processing complexity increases
Solution Approach 1:
The patent segments the image into macro-pixels with regular geometric patterns (e.g., 2x2 or 4x4 blocks), which simplifies the processing logic compared to arbitrary region segmentation. This regular segmentation reduces control complexity while achieving reduced processing time.
Solution Approach 2:
The patent uses lookup tables (LUTs) to store pre-computed conversion values and patterns. By copying and applying these pre-computed values to macro-pixels, the patent reduces real-time computational complexity while maintaining processing speed advantages.
4Area of stationary object
If macro-pixel processing is used, then area usage is reduced, but image quality may be compromised
Solution Approach 1:
The patent applies different processing strategies to different regions or types of macro-pixels. By maintaining higher processing fidelity in critical areas and using more aggressive compression in less critical areas, the patent achieves acceptable overall image quality while reducing area usage through macro-pixel processing.
Solution Approach 2:
The patent processes only essential color conversion operations at the macro-pixel level, omitting some finer-detail processing that would be performed on individual pixels. This partial action approach reduces area usage while maintaining sufficient image quality for most applications.
Data Source
AI summary
Tone mapping is performed by digital image processing circuitry on a macro-pixel basis. A luminance value of a macro-pixel of a digital image in a color space is determined. The macro-pixel includes a plurality of individual pixels. Respective tone-mapping gain values of each pixel of the macro-pixel are determined based on the determined luminance value of the macro-pixel. The determined tone-mapping gains are applied to the respective pixels of the macro-pixel. The color space may be a CFA color space, such as a Bayer color space.