Lossless Compression of Single-Channel Images Using Prediction Errors

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Solution Overview

Problem

Current lossless image compression techniques face challenges in achieving high compression ratios without incurring cycle-intensive processes that consume excessive power and time, particularly with the increasing file sizes from enhanced computing device sensors, necessitating efficient storage solutions for single-channel images.

Innovation Solution

The techniques involve pre-processing single-channel images by calculating predicted pixel intensity values based on neighboring values, separating prediction errors into distinct byte streams, and compressing these streams using Lempel-Ziv-Welch (LZW)-based compressors to enhance compression ratios, as well as interleaving pixel values from multiple images to further optimize compression.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If higher compression ratios are used to increase storage space, then compression efficiency is improved, but compression time and power consumption increase

Engineering Contradiction:

Improvestorage spaceVSAvoidcompression time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The patent segments the compression process into distinct stages: prediction stage (computing predicted pixel values from neighboring pixels) and compression stage (compressing prediction errors using LZW algorithm). This segmentation allows the heavy lifting of prediction to be done separately, enabling faster compression with good ratios

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary prediction of pixel values based on neighboring pixels before actual compression. This preliminary action reduces the complexity of the data that needs to be compressed, achieving higher compression ratios without proportionally increasing compression time

Inventive Principle:

Principle #10Preliminary action

2Quantity of substance

If higher compression ratios are used to increase storage space, then compression efficiency is improved, but power consumption increases

Engineering Contradiction:

Improvestorage spaceVSAvoidpower consumption

Core Design Contradiction:

Quantity of substanceVSUse of energy by moving object

Solution Approach 1:

By segmenting the compression into prediction and compression phases, the system can use simpler, less power-intensive operations during compression rather than relying on complex transform-based methods, reducing overall power consumption while maintaining compression efficiency

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The prediction step uses locally available neighboring pixel data to generate predictions, making the process self-contained and avoiding the need for external resources or complex computations, thereby reducing power requirements

Inventive Principle:

Principle #25Self-service

3Productivity

If cycle-efficient compression techniques are used to reduce power and time consumption, then processing efficiency is improved, but compression ratios decrease

Engineering Contradiction:

Improvecompression speedVSAvoidstorage space

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The patent changes the parameter being compressed from raw pixel values to prediction errors. This parameter transformation enables efficient compression because prediction errors have smaller magnitude and more structure, allowing fast compression with good ratios

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The prediction step acts as an intermediary between the image data and the compression algorithm. It processes the data in a way that makes it more suitable for compression, enabling both speed and efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If cycle-efficient compression techniques are used to reduce power and time consumption, then processing efficiency is improved, but compression ratios decrease

Engineering Contradiction:

Improvecompression speedVSAvoidstorage space

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The patent replaces traditional mechanical compression methods (like DCT-based approaches) with a prediction-error based system. This substitution uses simpler arithmetic operations instead of complex transforms, achieving both speed and compression efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10798420B2Lossless compression techniques for single-channel images

Publication Date: 2020.10.06 APPLE INC

AI summary

Disclosed herein are techniques for performing lossless compression of single-channel images (e.g., grayscale images). A first technique involves pre-processing an isolated (i.e., one) single-channel image for compression. In particular, the first technique involves calculating predicted pixel intensity values (within the single-channel image) based on neighboring pixel intensity values (also within the single-channel image). Bit values of the error margins of the predicted pixel intensity values are separated into two different byte streams according to a particular ordering, whereupon the byte streams are separately compressed (e.g., using a Lempel-Ziv-Welch (LZW)-based compressor) and joined together to produce a compressed single-channel image. A second technique involves pre-processing a group of single-channel images into one single-channel image for compression. In particular, the second technique involves interleaving pixel intensity values of the single-channel images to produce an interleaved single-channel image, and subsequently compressing the interleaved single-channel image (e.g., using an LZW-based compressor).