Image Compression Algorithm Selection by Frame Similarity
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Solution Overview
Problem
Existing image processing technologies lack the ability to dynamically adjust compression algorithms and loss factors based on the similarity between consecutive images, leading to suboptimal compression efficiency and quality.
Innovation Solution
An image processing apparatus and method that selects and adjusts compression algorithms and loss factors based on the similarity between previous and current images, using a plurality of algorithms and loss factors to optimize compression according to the fluctuation level, and transmits compressed images with adjustable quality in response to control signals from external terminals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single compression algorithm is used for all images, then the device complexity is reduced, but the compression efficiency and quality deteriorate
Solution Approach 1:
The system dynamically selects compression algorithms based on the similarity between current and reference images. When images are highly similar, a simpler algorithm is used; when they differ significantly, a more sophisticated algorithm is selected, making the system adaptive rather than static
Solution Approach 2:
The system changes the compression algorithm parameter based on image similarity metrics. By calculating the difference between consecutive images and adjusting the algorithm selection accordingly, the system optimizes compression performance for different image conditions
2Productivity
If a high loss factor is used for compression, then the compression ratio is improved, but the image quality deteriorates
Solution Approach 1:
The loss factor is dynamically adjusted based on image similarity. For images that are very similar to the reference image, a higher loss factor is applied since the difference will be small; for dissimilar images, a lower loss factor preserves more detail
Solution Approach 2:
The system changes the loss factor parameter according to the calculated image similarity. This parameter adjustment allows the system to optimize between compression ratio and quality based on the actual content being compressed
3Productivity
If image compression is performed without considering similarity, then the processing speed is maintained, but the compression efficiency deteriorates
Solution Approach 1:
The system performs a preliminary similarity assessment between the current image and reference image before applying compression. This preliminary step allows the system to choose the most efficient compression path, avoiding unnecessary complex processing for similar images
Data Source
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AI summary
An image processing apparatus and method capable of compressing a current image using one of a plurality of compression algorithms based upon similarity (or fluctuation level) between a previous image and the current image are discussed.