Denoiser-Based Universal Lossy Data Compression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current lossy compression methods are computationally complex and impractical, and existing universal lossy compression techniques are not optimal, leading to poor compression ratios and increased distortion in reconstructed signals.
Innovation Solution
A lossy compression method that employs a denoising technique to remove high entropy features from data, followed by universal lossless compression, effectively cascading a denoising process with either lossy or lossless compression methods to improve computational efficiency and reduce distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If practical universal lossy compression methods are used, then computational complexity is reduced, but compression optimality and performance deteriorate
Solution Approach 1:
The compression system is segmented into two distinct functional modules: a denoising module that removes high-entropy features and a compression module that compresses the denoised data. This segmentation allows each module to specialize in its function, achieving near-optimal compression performance while maintaining practical computational complexity.
Solution Approach 2:
The denoising operation is performed as a preliminary action before compression. By removing high-entropy features and noise from the data beforehand, the subsequent compression operation works on cleaner, lower-entropy data, significantly improving compression ratios without requiring complex compression algorithms.
2Productivity
If lossy compression is applied, then compression ratio is improved, but signal distortion increases
Solution Approach 1:
The invention converts the harmful effect of noise and high-entropy features in the data into a benefit. By identifying and removing these high-entropy components through denoising, the system achieves better compression ratios while preserving the essential low-entropy structure of the signal, thereby reducing perceptible distortion in the reconstructed signal.
Solution Approach 2:
The denoising process changes the entropy parameter of the data by removing high-entropy features. This parameter transformation converts noisy, high-entropy data into clean, low-entropy data that is more compressible and results in lower distortion after compression and decompression.
3Productivity
If data with high entropy features is compressed, then compression efficiency decreases, but removing features alters the original data
Solution Approach 1:
The denoising operation serves as an intermediary process between the original data and the compression operation. It mediates by removing high-entropy features that would otherwise reduce compression efficiency, while preserving the essential information contained in low-entropy features, thus improving compression efficiency without significant loss of important data.
Data Source
AI summary
Various embodiments of the present invention provide a compression method and system that compresses received data by first denoising the data and then losslessly compressing the denoised data. Denoising removes high entropy features of the data to produce lower entropy, denoised data that can be efficiently compressed by a lossless compression technique. One embodiment of the invention is a universal lossy compression method obtained by cascading a denoising technique with a universal lossless compression method. Alternative embodiments include methods obtained by cascading a denoising technique with one or more lossy or lossless compression methods.