Adaptive Quantization for Image Compression
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Solution Overview
Problem
Existing image compression methods struggle to achieve optimal compression ratios while maintaining acceptable image quality, particularly in contexts where human perception is a key factor.
Innovation Solution
The method involves generating coefficients indicative of image contents at various spatial frequencies, scaling these coefficients using adaptive scaling factors, and iteratively assessing multiple quantization levels to determine the lowest level that satisfies an image quality threshold, thereby minimizing encoded image size while preserving quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression methods are used to reduce image size, then compression ratio is improved, but image quality deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting quantization thresholds and scaling factors based on image characteristics and human visual perception models. Different quantization thresholds are applied to different spatial frequency components, with lower thresholds for high-frequency components that are less perceptible to humans, thereby achieving higher compression ratios while maintaining acceptable image quality.
Solution Approach 2:
The patent implements local quality by applying different compression strengths to different regions of the frequency spectrum. High-frequency components (which are less important to human perception) are compressed more aggressively than low-frequency components, allowing optimized compression where it matters least to the human eye while preserving quality where it matters most.
2Quantity of substance
If quantization thresholds are increased to reduce encoded image size, then compression ratio is improved, but post-quantization energy increases
Solution Approach 1:
The patent employs feedback mechanisms where the encoder estimates post-quantization energy and uses this information to adaptively adjust quantization thresholds. The system continuously monitors the balance between compression ratio and energy loss, using feedback from quality assessment models to optimize the quantization process in real-time.
Solution Approach 2:
The patent applies dynamics by making quantization thresholds variable rather than fixed. The thresholds dynamically adapt based on image content, spatial frequency, and current compression requirements, allowing the system to optimize the trade-off between encoded size and energy loss for each specific encoding scenario.
Data Source
AI summary
Methods are provided that exhibit increased quality and compression factor for compressing images. The methods can include generating a set of coefficients indicative of image contents of a block of image pixels at a plurality of spatial frequencies. The set of coefficients is scaled to generate a first set of scaled coefficients. An assessment is performed for a plurality of quantization levels, which includes quantizing a subset of the first set of scaled coefficients according to respective quantization levels to generate a quantized subset of the first set of scaled coefficients and determining a post-quantization energy of the quantized subset of the first set of scaled coefficients. Based on the assessment of the plurality of quantization levels, a scaled and quantized version of the set of coefficients is generated. An encoded version of the image based on the scaled and quantized version of the set of coefficients is generated.