Adaptive DCT Sharpener for Compressed Image Quality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image compression methods often result in reduced compressibility and increased file sizes when enhancing image quality, due to decompression and re-compression processes, which can introduce artifacts and increase computational costs.
Innovation Solution
A method that directly modifies quantization factors and coefficients of compressed images to enhance sharpness and quality without decompressing them, using scaling factors selected based on image quality, and updating these factors through training with reference images to minimize differences between enhanced and original images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If decompression and re-compression processes are used to enhance image quality, then image sharpness is improved, but file size increases and compressibility decreases
Solution Approach 1:
The patent modifies quantization parameters directly in the compressed domain to enhance image sharpness. By adjusting the quantization table values and applying scaling factors to DCT coefficients, the system improves perceived image quality without decompressing the entire image, thereby avoiding file size increase while maintaining compressibility.
Solution Approach 2:
The patent extracts and processes only the essential components (quantization parameters and DCT coefficients) from the compressed image data. By working with these extracted parameters rather than the full decompressed image, the system achieves sharpness enhancement while preserving the compressed format and avoiding unnecessary file size expansion.
2Manufacturing precision
If decompression and re-compression processes are used to enhance image quality, then image sharpness is improved, but computational cost increases
Solution Approach 1:
The patent performs sharpness enhancement by directly modifying quantization parameters and DCT coefficients in the compressed domain. This approach avoids the computationally expensive steps of full decompression and re-compression, significantly reducing processing power requirements while achieving the desired sharpness improvement through targeted parameter adjustments.
Solution Approach 2:
The patent extracts only the necessary quantization parameters and coefficient data from the compressed image for processing. By working with these minimal extracted elements rather than processing the entire decompressed image, the system drastically reduces computational overhead while maintaining the ability to enhance sharpness effectively.
3Manufacturing precision
If decompression and re-compression processes are used to enhance image quality, then image sharpness is improved, but artifacts are introduced
Solution Approach 1:
The patent enhances sharpness by carefully adjusting quantization parameters and applying scaling factors to DCT coefficients within the compressed domain. This controlled parameter modification approach avoids the aggressive compression and decompression cycles that generate artifacts, achieving sharpness improvement while preserving image fidelity and minimizing harmful compression effects.
Data Source
AI summary
Methods are provided for sharpening or otherwise modifying compressed images without decompressing and re-encoding the images. An overall image quality is determined based on the source of the compressed image, the quantization table of the compressed image, or some other factor(s), and a set of scaling factors corresponding to the image quality is selected. The selected scaling factors are then applied to corresponding quantization factors of the image's quantization table or other parameters of the compressed image that describe the image contents of the compressed image. The scaling factors of a given set of scaling factors can be determined by a machine learning process that involves training the scaling factors based on training images determined by decompressing and then sharpening or otherwise modifying a source set of compressed images. These methods can provide improvements with respect to encoded image size and computational cost of the image modification method.