Image Compression via Boundary Area Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image compression techniques fail to optimize compression rates and image quality when dealing with telop images, as they assume frames are identical or compress objects uniformly, leading to decreased compression efficiency and image quality when frames differ or contain mixed content requiring varying quality levels.
Innovation Solution
An image compression device and method that detects boundary and non-boundary areas within an image, compressing non-boundary areas in a lossy mode and boundary areas in a lossless mode to minimize differences before and after expansion, allowing for adaptive compression based on area type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single compression mode is applied to the entire image, then the compression rate can be improved, but the image quality deteriorates in critical areas
Solution Approach 1:
The image is divided into multiple regions with different compression characteristics. The boundary detection unit identifies boundary regions that require high quality, while non-boundary regions can use aggressive compression. This segmentation allows different compression modes to be applied to different parts of the image, resolving the contradiction between overall compression rate and local image quality.
Solution Approach 2:
Different compression qualities are applied to different regions of the image based on their importance. Boundary regions, which contain critical structural information, are compressed with higher quality settings, while non-boundary regions use lower quality settings to maximize compression. This local quality approach maintains overall image quality while improving compression rate.
2Device complexity
If uniform compression is applied to all objects, then the processing complexity is reduced, but compression efficiency decreases when objects have different characteristics
Solution Approach 1:
The compression system dynamically adapts its parameters based on the detected image characteristics. The boundary detection unit analyzes the image content and automatically adjusts compression settings for different regions. This dynamic adaptation allows the system to optimize compression efficiency for various object types without requiring manual configuration or complex pre-processing.
3Quantity of substance
If aggressive compression is used to reduce data size, then storage efficiency improves, but image quality after expansion deteriorates
Solution Approach 1:
The image data is segmented into boundary and non-boundary regions, with different compression strategies applied to each. Boundary regions use milder compression to preserve critical structural information, while non-boundary regions use aggressive compression to reduce data size. This segmentation resolves the contradiction between data size reduction and image quality preservation after expansion.
Data Source
AI summary
The present invention aims at improving an image compression rate and enhancing quality of images after expansion. An image compression device generates compressed data formed by compressing an image. The device includes a boundary area detection section for detecting boundary areas which include both of internal and external parts of an object rendered in the image, from among plural areas included in the image; a non-boundary area data generation section for, by compressing non-boundary areas which are not the boundary areas in the image, in a predetermined compression mode, generating partial compressed data in the compressed data, corresponding to the non-boundary areas; and a boundary area data generation section for generating partial compressed data in the compressed data, corresponding to the boundary areas, from the boundary areas, in a mode in which difference between images before compression and after expansion is less in comparison with a case where the boundary areas are compressed in the predetermined compression mode.