Comparison of Lossless Image Compression Techniques based on Context Modeling

Table of Contents

1. INTRODUCTION

1.1 Data compression

1.2 Basic concepts in image compression

1.2.1 Lossless and lossy cases

1.2.2 Measures of compression

1.2.3 Paradigm of compression

1.2.4 Arithmetic coding

1.3 Thesis objectives

1.4 Contents of thesis' chapters

2. CONTEXT MODELING

2.1 Fundamentals

2.2 Context Tree

2.2.1 Structure of context tree

2.2.2 Static and semi-adaptive approaches

2.2.3 Construction of an initial context tree

2.2.4 Pruning of context tree

3. LOSSLESS IMAGE COMPRESSION TECHNIQUES

3.1 JPEG2000 standard

3.2 CALIC

3.2.1 CALIC Frame Structure

3.2.2 Neighborhood pixels involved

3.2.3 LINEAR PREDICTION - GAP

3.2.4 CODING CONTEXT

3.2.5 CONTEXT MODELING FOR ADAPTIVE ERROR FEEDBACK

i. Contexts Formation

ii. Error Feedback

3.2.6 ENTROPY CODING OF PREDICTION ERRORS

3.2.6.1 Error Sign Flipping

3.2.6.2 Remapping Errors

3.2.6.3 Histogram Tail Truncation

A. Proposed modification

3.2.7 Continuous-tone mode results

3.2.8 BINARY MODE

A. Proposed modification

3.3 General Context Tree based on Intensity

3.3.1 Context template

3.3.2 Compression scheme

3.3.3 GCT-I Simulation results

3.4 COMPARATIVE ANALYSIS OF CONSIDERED TECHNIQUES

4. CONCLUSIONS AND SUGGESTION FOR FUTURE WORK

4.1 Conclusions

4.2 Suggestion for Future Work

Objectives & Research Topics

The primary objective of this thesis is to analyze and compare various lossless compression algorithms, with a specific focus on those utilizing context modeling through tree structures. The research explores methods to improve performance in both continuous-tone and binary modes by proposing modifications to existing algorithms, such as CALIC, and evaluating the GCT-I algorithm on medical imaging datasets.

• Comparison of standard lossless image compression techniques (JPEG2000, CALIC, GCT-I).
• Development of modifications to the CALIC algorithm for continuous-tone mode via error histogram truncation.
• Refinement of CALIC in binary mode by eliminating the error feedback mechanism.
• Performance evaluation and comparative analysis on natural smooth and medical images.

Excerpt from the Book

Summary of Chapters

1. INTRODUCTION: This chapter introduces data compression concepts, distinguishes between lossless and lossy methods, and outlines the thesis objectives and scope.

2. CONTEXT MODELING: This section details the theoretical fundamentals of context modeling, including the structure, construction, and pruning of context trees for probability estimation.

3. LOSSLESS IMAGE COMPRESSION TECHNIQUES: This chapter provides a comprehensive analysis of JPEG2000, CALIC, and GCT-I, proposing specific modifications to improve their performance on different image types.

4. CONCLUSIONS AND SUGGESTION FOR FUTURE WORK: This final chapter summarizes the research findings, highlights the performance improvements achieved by the proposed modifications, and offers suggestions for future developments.

Keywords

Lossless compression, Context modeling, Image compression, CALIC, GCT-I, JPEG2000, Error feedback, Entropy coding, Histogram truncation, Data compression, Image processing, Medical images, Predictive coding, Bit-rate, Context tree.

Frequently Asked Questions

What is the core focus of this thesis?

The thesis focuses on analyzing and comparing lossless image compression algorithms that rely on context modeling and tree-based structures.

Which compression algorithms are examined?

The research primarily evaluates and compares the JPEG2000 standard, the CALIC (Context-based, Adaptive, Lossless Image Codec), and the GCT-I (General Context Tree based on Intensity) method.

What is the primary goal of the study?

The main goal is to improve the compression efficiency of existing methods, specifically by modifying the CALIC algorithm for continuous-tone and binary image modes and assessing GCT-I on medical datasets.

What methodology is employed for these comparisons?

The study uses experimental simulation on specific test sets (natural smooth images and medical images) and evaluates performance based on criteria such as bit-rate (bits per pixel) and compression efficiency.

What does the main part of the work cover?

The main part covers the theory of context modeling, a detailed examination of JPEG2000 and CALIC architectures, the implementation of histogram tail truncation and feedback elimination, and the development of the GCT-I approach.

Which keywords define this work?

The work is characterized by terms such as Lossless compression, Context modeling, CALIC, GCT-I, Error feedback, and Entropy coding.

Why is the error feedback mechanism in CALIC problematic for binary images?

The error feedback mechanism is designed for continuous-tone images; when triggered in binary-like regions (where pixels have widely separated gray levels), it can lead to a decrease in the compression ratio.

How does the proposed modification for binary mode work?

The study proposes eliminating the positive feedback mechanism specifically when the algorithm triggers an escape sequence due to a different gray level in binary images, thereby improving overall compression performance.

What is the specific advantage of the GCT-I algorithm?

GCT-I is particularly effective for medical images, which are characterized by a small number of colors and sharp color transitions where traditional linear predictive techniques often fail.