Image Compression Entropy Analysis for MFD Throughput
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Solution Overview
Problem
Current data compression methods in high-speed multi-function devices (MFDs) are slow and degrade system performance when increasing bits per pixel, leading to sub-par image quality and throughput, especially when compressing images with varying content using fixed compression settings.
Innovation Solution
An image processing method that computes entropy values for image segments, allowing for dynamic application of a compression acceleration factor to optimize compression speed and ratio, segmenting images into non-overlapping segments and using LZ4 or JPEG compression based on entropy measurements to adapt compression settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If software compression schemes are used to compress digital image data before storing in EPC memory, then memory usage is reduced, but compression speed becomes the slowest component in the software image path
Solution Approach 1:
The image is divided into multiple segments, and entropy analysis is performed on each segment independently. This allows parallel processing of different segments, significantly improving compression speed while maintaining effective compression ratios. The segmentation enables the system to process high-bit-depth images without becoming a bottleneck in the software image path.
Solution Approach 2:
The compression system dynamically adjusts compression parameters based on real-time entropy analysis of image segments. By calculating entropy values and using them to determine acceleration factors, the system adapts compression intensity to match actual image content complexity, optimizing both speed and compression efficiency for varying image types.
2Manufacturing precision
If the number of bits per pixel increases from 1 bit to 2 bits, 4 bits, or 8 bits per pixel, then image quality improves, but system performance degrades below rated speed
Solution Approach 1:
Entropy analysis is performed preliminarily on image segments before compression to predict compressibility. This pre-analysis allows the system to select appropriate compression strategies in advance, ensuring that high-bit-depth images are processed efficiently without degrading system performance below rated speed.
Solution Approach 2:
The system changes compression parameters dynamically based on entropy values calculated from image segments. By adjusting acceleration factors according to measured entropy, the system maintains optimal compression performance across varying bit-depths and image qualities, preventing performance degradation.
3Device complexity
If the same compression parameters are used for all images regardless of content, then processing is simplified, but image quality and throughput are not optimized
Solution Approach 1:
Different compression parameters are applied to different image segments based on their local entropy characteristics. This local adaptation ensures that each segment is compressed with optimal settings for its specific content type, improving overall image quality and throughput while maintaining manageable processing complexity through automated entropy-based classification.
Data Source
AI summary
An image processing system and method are described which use entropy values in processing the image. For each of a plurality of segments of an image, the segment is considered as a stream of bits and a bit difference is computed between pairs of sequential bit sequences in the stream of bits. An entropy value of the segment is computed as a function of the bit differences. The image is processing based on the computed entropy values, which may include compressing some or all of the image segments using an acceleration factor that is based on the computed entropy value for the segment. In this way, differences in entropy, which correlate with the compressibility of the image segment, can ensure an optimal compression of the segment, or in some cases, no compression.