Psychovisual Image Compression for Bandwidth Optimization
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Solution Overview
Problem
Conventional image compression methods often result in perceptual loss of detail and inefficient bandwidth usage, as they fail to effectively prioritize luminance over chroma in high-resolution images.
Innovation Solution
The implementation of psychovisual image compression techniques that select appropriate compression processes based on pixel data characteristics, discarding unnecessary bits to achieve a fixed compression ratio with minimal detail loss, and can be performed in hardware on scan lines captured by image sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional image compression methods are used, then compression is achieved, but perceptual loss of detail occurs
Solution Approach 1:
The patent applies different compression ratios to different regions of the image based on their visual importance. High-frequency detail regions maintain higher compression ratios while low-frequency regions use lower compression ratios, optimizing the balance between compression efficiency and perceptual quality preservation.
Solution Approach 2:
The system dynamically adjusts compression parameters including compression ratio, quantization step size, and bit allocation based on local image characteristics such as frequency content and visual importance, enabling optimized compression that minimizes perceptual loss.
2Quantity of substance
If conventional image compression methods are used, then compression is achieved, but bandwidth usage is inefficient
Solution Approach 1:
The compression system dynamically adapts its parameters in real-time based on image content characteristics, adjusting compression strength and bit allocation to optimize bandwidth usage efficiency for each specific image rather than applying a fixed compression level.
Solution Approach 2:
The system changes compression parameters such as quantization steps and bit rates based on the statistical properties of the image data, achieving more efficient bandwidth utilization by allocating bits according to actual image needs rather than using uniform compression.
3Device complexity
If fixed compression ratio is applied, then compression is simplified, but perceptual quality varies
Solution Approach 1:
Instead of applying a single fixed compression ratio throughout the image, the system divides the image into regions and applies different compression levels to each region based on its visual characteristics, maintaining consistent perceptual quality while managing complexity through localized processing.
Solution Approach 2:
The system dynamically adjusts compression parameters based on local image characteristics rather than using a static fixed ratio, enabling consistent perceptual quality across different image regions while adapting the compression process to local needs.
Data Source
AI summary
Psychovisual image compression techniques are disclosed that compress pixel data by a fixed compression ratio with little or no perceptual loss of detail. In some implementations, a psychovisual compression process is selected among several psychovisual compression processes based on characteristics of the pixel data. Compression is achieved during encoding by discarding psychovisually unnecessary bits from the pixel data. The psychovisual compression processes can be implemented in hardware and operate on scan lines of pixels captured by the image sensor. The psychovisual compression techniques can be used with image compression techniques to compress further the pixel data.