Date of Award
2018
Document Type
Thesis
Degree Name
Master of Science in Engineering (MSE)
Department
Electrical and Computer Engineering
Committee Chair
W. David Pan
Committee Member
B. Earl Wells
Committee Member
Seong-Moo Yoo
Subject(s)
Data compression (Computer science), Image compression--Standards.
Abstract
The creation of and demand for large-scale image data grows increasingly fast, resulting in a need for efficient image compression. This work focuses on improving the efficiency of lossless compression of binary images. To this end, we propose the use of the following two optimization algorithms, which search for either the best combination of scan directions for uniform blocks, or search for the best partitions of the input image into non-uniform blocks, with the goal of allowing for more efficient compression of the resulting sequences of intervals between successive symbols of the same kind. The first algorithm is the Binary Particle Swarm Optimization (BPSO) algorithm, which is shown to offer increasingly better image compression with additional iterations. The other algorithm is the Tree-based Search algorithm, which searches for the best grid structure for adaptively partitioning the image into blocks of varying sizes. Extensive simulations of these two search algorithms on various datasets demonstrated that we can achieve significantly higher compression on average than various standard binary image compression methods such as the JPEG 2000 and JBIG2 standards.
Recommended Citation
Hooda, Reetu, "Search and optimization algorithms for binary image compression" (2018). Theses. 248.
https://louis.uah.edu/uah-theses/248