Image Compression via Quality Factor Curve Fitting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image compression methods require multiple iterations with an improperly set initial quality factor, leading to long compression times and low efficiency.
Innovation Solution
Determine an optimum quality factor through curve fitting using existing quality and similarity scores, allowing for efficient image compression by establishing a functional relationship between quality factor and similarity score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple iterations with improper initial quality factor are performed, then image compression quality is improved, but compression time increases and efficiency decreases
Solution Approach 1:
The patent applies preliminary action by pre-establishing a functional relationship between quality factor and similarity score through curve fitting using existing data. This pre-computed relationship serves as a guide to directly determine the optimal quality factor without needing to perform multiple trial compressions, thereby avoiding time-consuming iterations while ensuring compression quality.
Solution Approach 2:
The patent utilizes feedback by incorporating similarity score evaluation into the quality factor determination process. By using the functional relationship derived from feedback data (similarity scores from previous compressions), the system can directly calculate the optimal quality factor that achieves desired compression quality, eliminating the need for repeated trial-and-error iterations.
2Ease of operation
If empirical initial quality factor is set according to experience, then compression process is simple, but compression efficiency becomes low when improper values are used
Solution Approach 1:
The patent applies parameter changes by transforming the quality factor selection from an empirical, experience-based approach to a data-driven approach using curve fitting. The functional relationship between quality factor and similarity score is established through mathematical modeling, allowing the system to automatically determine optimal parameters based on actual compression data rather than relying on manual tuning or preset values.
Data Source
AI summary
The present disclosure discloses method and apparatus for image compression. The method includes: acquiring a threshold of a similarity score of an image; acquiring a first quality factor of the image, a first similarity score corresponding to the first quality factor, a second quality factor of the image, and a second similarity score corresponding to the second quality factor; obtaining a functional relationship between quality factor and similarity score of the image by means of curve fitting using the first quality factor, the first similarity score, the second quality factor, and the second similarity score; determining an optimum quality factor of the image according to the functional relationship between the quality factor and the similarity score and the threshold of the similarity score; and compressing the image according to the determined optimum quality factor. With the present disclosure, iterations in image compression can be completed in a very short time.