Multiple-Channel Image Compression via Alpha Separation and Predictive Encoding
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Solution Overview
Problem
Current image compression techniques face challenges in achieving high compression ratios while minimizing power consumption and latency, especially in devices that store large collections of frequently accessed images like emoticons, where computational overhead and storage space are concerns.
Innovation Solution
The method involves pre-processing multiple-channel images by separating alpha sub-pixels, applying invertible transformations, identifying predictive functions, and encoding prediction differentials into data streams before compressing them using Lempel-Ziv-Welch (LZW)-based compressors to enhance compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If higher compression ratios are used to increase storage space, then compression ratio is improved, but power consumption and processing time increase
Solution Approach 1:
The patent applies preliminary action by performing pre-processing operations (separating alpha sub-pixels, applying invertible transformations, identifying predictive functions, and encoding prediction differentials) before the actual compression step. This prepares the image data in advance to be more compressible, allowing the use of simpler, more power-efficient compression algorithms while still achieving high compression ratios.
2Quantity of substance
If higher compression ratios are used to increase storage space, then compression ratio is improved, but processing time increases
Solution Approach 1:
The pre-processing steps are performed once during image preparation, transforming the image into a format that requires less computational effort during actual compression and decompression operations. This reduces processing time for frequently accessed images while maintaining high compression ratios.
Solution Approach 2:
The patent segments the image processing into distinct stages: alpha channel separation, invertible transformation, predictive function identification, and differential encoding. This segmentation allows each stage to be optimized independently and enables parallel processing where applicable, reducing overall processing time.
3Use of energy by moving object
If cycle-efficient compression techniques are used to reduce power consumption, then power consumption is reduced, but compression ratio decreases
Solution Approach 1:
By performing the computationally intensive pre-processing operations once during image preparation, the patent enables the use of simpler, more power-efficient compression algorithms during actual compression. The pre-processed data structure allows cycle-efficient compressors to achieve high compression ratios without requiring repeated complex computations.
Data Source
AI summary
Disclosed are techniques for pre-processing an image for compression, e.g., one that includes a plurality of pixels, where each pixel is composed of sub-pixels that include at least an alpha sub-pixel. First, the alpha sub-pixels are separated into a first data stream. Next, invertible transformations are applied to the remaining sub-pixels to produce transformed sub-pixels. Next, for each row of the pixels: (i) identifying a predictive function that yields a smallest prediction differential total for the row, (ii) providing an identifier of the predictive function to a second data stream, and (iii) converting the transformed sub-pixels of the pixels in the row into prediction differentials based on the predictive function. Additionally, the prediction differentials for each of the pixels are encoded into first and second bytes that are provided to third and fourth data streams, respectively. In turn, the various data streams are compressed into a compressed image.