Dynamic Image Compression Ratio Selection Based on Noise Metrics
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Solution Overview
Problem
Digital image compression often reduces image quality and introduces artifacts, especially in images with high noise levels, and existing methods for determining optimal compression ratios are calculation-intensive and time-consuming.
Innovation Solution
A method for selecting an image compression ratio based on image noise metrics derived from readily available image characteristics like gain and lux, allowing for differential compression of image regions to minimize file size without significantly impacting visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image compression is applied to reduce file size, then storage efficiency improves, but image quality deteriorates due to compression artifacts
Solution Approach 1:
The patent applies different compression ratios to different regions of the image based on local noise characteristics. High-noise regions are compressed more aggressively while low-noise regions maintain higher quality, resolving the contradiction by making compression quality spatially variable rather than uniform across the entire image.
Solution Approach 2:
The patent dynamically changes the compression ratio parameter based on the measured noise level in each image or region. By adjusting this key parameter according to actual image characteristics, the system achieves optimal balance between file size reduction and quality preservation for different images.
2Productivity
If noise analysis is performed to determine optimal compression ratio, then compression efficiency improves, but computational complexity increases
Solution Approach 1:
The patent performs noise analysis as a preliminary step before compression, using the noise metric to guide subsequent compression decisions. This preliminary characterization enables more efficient compression by avoiding unnecessary quality preservation in high-noise areas while maintaining quality where needed.
Solution Approach 2:
The patent introduces an intermediary noise metric that correlates with image quality but is computationally simpler to calculate than full quality assessment. This intermediary measure serves as a proxy that enables compression decisions without requiring complex computational analysis.
Data Source
AI summary
An image is compressed according to a compression ratio selected based on a compression metric. In one embodiment, the compression metric is based on image characteristics indicative of the amount of image noise in an image, such as gain and lux. Greater compression ratios are used for image having compression metrics indicating a higher degree of noise. Because an image with higher image noise levels already has a reduced visual quality, the impact of higher compression is less significant as compared to the impact of compression on images having low image noise. In an embodiment, an image is divided into a number of regions, for each of which a compression metric and corresponding compression ratio is determined, so that regions of the image having high image noise may be compressed more than regions having low image noise.