Reetu Hooda

Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Electrical and Computer Engineering

Committee Chair

W. David Pan

Committee Member

Vineetha Menon

Committee Member

B. Earl Wells

Committee Member

Sivaguru S. Ravindran

Committee Member

Seong-Moo Yoo


Data compression (Computer science), Image compression--Standards, Video compression--Standards


Advancement in high resolution devices used to capture images and videos has put strain on data storage and transmission. For their practical usage, efficient data compression techniques becomes essential. In this context, we conducted a study on compression of two media formats, including ROI maps of hyperspectral images, and 3D point clouds. For lossless compression of ROI maps, we proposed novel schemes that can achieve high compression ratios by partitioning the intricate regions of the image into smaller blocks. Our analysis of a linear predictive model applied on the original images showed that the resulting residual images tend to have lower entropy. We then proposed to use the DPSO algorithm that searched for the best combination of scan directions to achieve better compression. Simulation results on various data sets showed that the proposed scheme outperformed JBIG2, the international standard for binary image compression. Our work also advanced the research on lossy attribute compression of 3D point clouds in two aspects. First, we proposed an adaptive scheme based on 3D Sobel filters that can switch between the RAHT and Dyadic RAHT to achieve early termination. We showed that the proposed method was able to achieve noticeable compression gains over the all-dyadic approach. Second, we further improved the adaptive method by training a neural networks so that the switching would no longer depend on a threshold. Compared to the GPCC standardized method that uses only Dyadic transform throughout the point cloud, the proposed scheme can achieve cumulative BR-rate gains on various publicly available datasets.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.