Two-Dimensional Signal Encoding for Bayer Pattern G Components
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing encoding methods for Bayer pattern images struggle to efficiently encode green (G) components due to the lack of consideration for the correlation among neighboring G components.
Innovation Solution
A two-dimensional signal encoding device that calculates high-pass (GH) and low-pass (GL) coefficients using high-pass and low-pass filtering techniques, respectively, to encode these coefficients efficiently, thereby leveraging the correlation among G components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional encoding methods are used for Bayer pattern images, then encoding can be performed with simple processing, but G components cannot be efficiently encoded due to lack of consideration for correlation among neighboring G components
Solution Approach 1:
The patent divides the G component encoding into two separate processing paths: one for G0 pixels and one for G1 pixels. Each path applies different filtering operations (high-pass for G1, low-pass for G0) to exploit the spatial correlation between neighboring G components. This segmentation allows efficient encoding by processing each G component type according to its specific spatial relationship with neighbors, rather than treating all G components uniformly.
2Productivity
If high-pass and low-pass filtering are applied to calculate GH and GL coefficients, then spatial correlation among G components is reduced and compression efficiency is improved, but computational load increases
Solution Approach 1:
The patent applies different filtering operations to different spatial locations: G1 pixels undergo high-pass filtering to calculate GH coefficients, while G0 pixels undergo low-pass filtering to calculate GL coefficients. This local differentiation optimizes compression efficiency by exploiting the specific spatial correlation patterns at each location, while keeping the computational operations simple and localized to avoid excessive computational load.
Data Source
AI summary
The device for encoding a Bayer pattern image includes a two-dimensional high-pass filtering means for calculating a GH coefficient which is a high-pass component corresponding to a space coordinate of a G1 pixel, from the value of the G1 pixel and the values of four G0 pixels in the neighborhood of the G1 pixel; a two-dimensional low-pass filtering means for calculating a GL coefficient which is a low-pass component corresponding to a space coordinate of a G0 pixel, from the value of the G0 pixel and the values of four GH coefficients in the neighborhood of the G0 pixel; a first encoding means for encoding a two-dimensional signal including the GH coefficient; and a second encoding means for encoding a two-dimensional signal including the GL coefficient.