Abstract

Modern video codecs rely on highly optimized block based intra and inter prediction frameworks,  in which block partitioning is determined through exhaustive rate distortion optimization (RDO)  searches. These exhaustive searches evaluate many possible block configurations to identify the  partitioning that minimizes overall rate distortion cost, and they have proven extremely effective  across diverse visual content. This thesis explores a complementary perspective by investigating  whether block partitioning can be guided more directly by content characteristics specifically,  texture complexity for intra prediction and motion complexity or motion density for inter  prediction.  For intra prediction, we introduce a texture adaptive approach that utilizes multi-scale Gabor  energy analysis to characterize local spatial structure. The intuition is that regions with more  intricate or high-frequency textures benefit from smaller block sizes, while smoother areas can be  represented efficiently with larger blocks. This content-aware partitioning is designed to work  alongside, rather than replace, traditional RDO, providing an informed initial structure that can  reduce reliance on exhaustive searches. For intra prediction, we propose a motion adaptive block  selection strategy driven by block level motion segmentation. Areas with complex or dense motion  are assigned to smaller block sizes, enabling more precise motion compensation, while temporally  stable regions favor larger partitions.   Across a variety of test sequences, the proposed framework demonstrates encouraging results,  achieving an average 1.1 dB PSNR improvement over the baseline reference configuration in  controlled experiments. These findings suggest that intelligently guiding block partitioning using  content complexity measures may offer a promising complementary direction for future research  in video coding.

Library of Congress Subject Headings

Digital video; Video compression; Video description

Publication Date

12-14-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical and Microelectronic Engineering, Department of

College

Kate Gleason College of Engineering

Advisor

Eli Saber

Advisor/Committee Member

Sohail Dianat

Advisor/Committee Member

Majid Rabbani

Campus

RIT – Main Campus

Plan Codes

EEEE-MS

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