Author ORCID Identifier

https://orcid.org/0009-0006-4809-3949

Semester

Fall

Date of Graduation

2025

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Piyush M. Mehta

Committee Co-Chair

Jeremy Dawson

Committee Member

Mohamed Hefeida

Committee Member

Nima Karimian

Committee Member

Laura Boucheron

Abstract

The rapid advancement of information technology and the exponential growth of digital communication have significantly increased the demand for efficient data compression techniques that reduce storage requirements, minimize bandwidth consumption, and accelerate data transmission—without substantially compromising data quality. This dissertation addresses these challenges by investigating and developing advanced learned image compression (LIC) methods, with a particular focus on lossy compression for both natural images and scientific imagery obtained from NASA’s Solar Dynamics Observatory (SDO) mission. Traditional image compression standards—such as JPEG, JPEG2000, BPG, and HEVC—rely on manually engineered transforms and heuristic rules, which often lack the adaptability required to accommodate diverse visual content and application-specific constraints. In contrast, learned image compression employs deep neural networks trained in an end-to-end manner, guided by principles from rate–distortion theory, to optimize the trade-off between compression efficiency and reconstruction fidelity.

In the first part of this dissertation, several technical challenges in developing neural image compression codecs for natural images (general-purpose) are addressed, including the design of expressive nonlinear transforms, accurate entropy modeling, and the integration of perceptually meaningful loss functions. To this end, several learned image compression frameworks are proposed, each introducing distinct design innovations: a Transformer-based nonlinear transform that captures both local and global dependencies, an advanced entropy model that improves probability estimation and coding efficiency, and a conditional diffusion-based generative framework that enhances the perceptual quality of reconstructed images. The second part focuses on the application of learned compression to imagery from NASA’s Solar Dynamics Observatory (SDO) mission. A learned video compression framework is developed to exploit both spatial and temporal redundancies in solar image sequences. Furthermore, an adaptive compression strategy is introduced to prioritize scientific relevance: images containing solar flare events are compressed at lower ratios to preserve critical information, whereas non-flare images are compressed more aggressively to maximize storage and transmission efficiency.

Collectively, these contributions advance the field of learned image compression across both general-purpose and scientific imaging domains, providing practical solutions for improving data transmission and storage efficiency in real-world and mission-critical environments.

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