Segmentation-Based Image Coding for HMI Quality
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Solution Overview
Problem
Existing video compression standards, such as H264, introduce information losses that are not noticeable in photo or video content but become visible in Human-Machine Interface (HMI) elements like buttons and menus, leading to reduced image quality and sharpness, especially when transitioning from PC to television applications.
Innovation Solution
A method that segments images into elementary blocks and applies either lossless or lossy compression based on the presence of high-frequency components, specifically using spatial filtering and thresholding to identify blocks with high detail, ensuring optimal quality for HMI areas and allowing lossy compression for other content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If lossy compression is applied to reduce data rate, then bandwidth requirements are reduced, but image quality deteriorates in HMI areas
Solution Approach 1:
The image is divided into multiple blocks, and each block is independently analyzed to determine whether it contains HMI elements. This segmentation allows different compression strategies to be applied to different regions of the same image, enabling lossless compression for HMI blocks and lossy compression for non-HMI blocks, thus resolving the contradiction between bandwidth reduction and image quality preservation.
Solution Approach 2:
Different compression qualities are applied to different regions of the image based on local content characteristics. HMI-containing blocks receive lossless compression to maintain sharp text and graphical elements, while blocks without HMI elements use lossy compression to reduce bandwidth. This local differentiation resolves the contradiction by optimizing quality only where necessary.
2Manufacturing precision
If lossless compression is applied to maintain image quality, then image quality is preserved, but data rate increases
Solution Approach 1:
The image is divided into multiple blocks, and each block is independently analyzed to determine whether it contains HMI elements. This segmentation allows different compression strategies to be applied to different regions of the same image, enabling lossless compression for HMI blocks and lossy compression for non-HMI blocks, thus resolving the contradiction between bandwidth reduction and image quality preservation.
Solution Approach 2:
Different compression qualities are applied to different regions of the image based on local content characteristics. HMI-containing blocks receive lossless compression to maintain sharp text and graphical elements, while blocks without HMI elements use lossy compression to reduce bandwidth. This local differentiation resolves the contradiction by optimizing quality only where necessary.
3Device complexity
If uniform compression is applied to all image blocks, then processing complexity is reduced, but rendering quality in HMI areas deteriorates
Solution Approach 1:
Before applying compression, the method performs a preliminary analysis of each block to detect the presence of HMI elements using spatial filtering and frequency domain analysis. This preliminary action identifies which blocks require lossless compression, allowing the system to maintain high rendering quality for HMI areas while simplifying processing for non-HMI areas through lossy compression.
Solution Approach 2:
Different compression qualities are applied to different regions of the image based on local content characteristics. HMI-containing blocks receive lossless compression to maintain sharp text and graphical elements, while blocks without HMI elements use lossy compression to reduce bandwidth. This local differentiation resolves the contradiction by optimizing quality only where necessary.
Data Source
Figure 1~4
AI summary
The invention relates to a method for encoding an image, said encoding being a mixed encoding with the possibility of using a first lossless (100) compression type and a second lossy (101) compression type, wherein said method comprises the operation that consists of dividing the image into a plurality of elementary blocks (201), characterised in that said method comprises the different additional steps of: determining which of the elementary blocks (302; 303) have a high level of detail; allocating the first compression type to each of the elementary blocks (302; 303) having a high level of detail; and allocating the second compression type to each of the elementary blocks (302; 303) not having a high level of detail.