Date of Award
6-2025
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2025
Degree Name
Master of Science (MS)
Department
Computer Science and Engineering
First Advisor
Nam Ling
Abstract
This thesis presents a comprehensive study on image compression, beginning with a review of both traditional and learning-based methods. Building upon the conventional Variational Autoencoder (VAE) framework, we explore the integration of Vision Transformer (ViT) architectures into the image compression pipeline to leverage their global modeling capabilities. Based on this, we propose Tiny-TLIC (Tiny Transformer-Based Lossy Image Compression), a lightweight yet effective model that introduces two novel components: Integrated Convolution and Self-Attention (ICSA) for enhanced feature representation, and a Multistage Context Model (MCM) for improved entropy estimation. To validate our approach, we train both a baseline Transformer-Based Image Compression (TIC) model and the proposed Tiny-TLIC. Extensive evaluations on the Kodak and other benchmark datasets demonstrate that Tiny-TLIC achieves competitive performance with significantly reduced model size, highlighting its potential for practical applications in resource-constrained environments.
Recommended Citation
Li, Chen, "Tiny-TLIC: Lightweight Transformer Based Lossy Image Compression" (2025). Computer Science and Engineering Master's Theses. 47.
https://scholarcommons.scu.edu/cseng_mstr/47
