Compressive Sensing Image Block Compression via Sinusoidal Signal Quantization
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Solution Overview
Problem
Existing image and video compression methods, such as JPEG, use lossy compression that fails to exploit the non-uniform distribution of AC coefficients, leading to suboptimal compression efficiency and image quality.
Innovation Solution
The method employs compressive sensing by computing the sum of sinusoidal signals at different frequencies for image blocks, quantizing and truncating these signals to create two compressed versions, allowing selection based on quality, enabling better compression ratios and maintaining consistent image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If standard JPEG compression is used, then compression is achieved, but image quality is lost and cannot be restored
Solution Approach 1:
The patent applies preliminary action by performing compressive sensing transformations and quantization before the actual compression process. The system pre-computes the sum of sinusoidal signals at different frequencies and amplitudes, then quantizes these transformed coefficients. This preliminary transformation allows the system to capture essential image information in a compressed form that can be reconstructed with minimal quality loss, addressing the fundamental JPEG limitation of irreversible quality degradation.
Solution Approach 2:
The patent utilizes parameter changes by transforming the image data from the spatial domain to the frequency domain through sinusoidal transformations. The system changes the representation parameters from pixel values to frequency coefficients, then applies quantization to these transformed parameters. This parameter transformation enables more efficient compression while preserving image quality, as the frequency domain representation captures essential visual information more effectively than direct spatial compression.
2Productivity
If compressive sensing is applied to improve compression ratios, then compression efficiency increases, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple blocks and processing each block independently through the compressive sensing transformation. The system segments the image data into manageable units, applies the sinusoidal transformation and quantization to each segment, then reassembles the compressed blocks. This segmentation approach reduces the computational complexity of processing large images while maintaining the benefits of compressive sensing, making the system more practical for real-world applications.
3Quantity of substance
If quantization and truncation are applied to reduce data size, then compression ratio improves, but image quality deteriorates
Solution Approach 1:
The patent implements feedback by comparing the compressed image quality against original image characteristics and adjusting the quantization and truncation parameters accordingly. The system evaluates the distortion introduced by compression and adapts the decoding process to compensate for quality loss. This feedback mechanism allows the system to maintain optimal image quality while achieving high compression ratios, as the decoding algorithm learns to reconstruct the image with minimal artifacts based on the compressed data characteristics.
Data Source
AI summary
A system and method for image and video compression using compressive sensing is provided. An embodiment method for compressing an image having a plurality of image blocks includes selecting an image block from the plurality of image blocks to compress, computing a sum of sinusoidal signals at different frequencies and amplitudes representation for the selected image block, quantizing the amplitudes of the sinusoidal signals at different frequencies, and saving the quantized amplitudes as a first compressed image block. The method also includes truncating the quantized amplitudes, thereby producing truncated quantized amplitudes, saving the truncated quantized amplitudes as a second compressed image block, and selecting either the first compressed image block or the second compressed image block as a final compressed image block. The selecting is based on a measure of the quality of the first compressed image block and the second compressed image block.