Graduation Semester and Year
2018
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical Engineering
First Advisor
Kamisetty R Rao
Abstract
The High Efficiency Video Coding (HEVC) standard has achieved best coding efficiency as compared to previous H.264/AVC standard. But the computational time of HEVC encoder has increased mainly because of the hierarchical quad-tree based structure, recursive search for finding the best coding units, and the exhaustive prediction search up-to 35 modes. These advances improve the coding efficiency, but result into a very high computational complexity. Furthermore selecting the optimal modes among all prediction modes are necessary for the subsequent rate distortion optimization process.Therefore we propose a convolutional neural network (CNN) based algorithm which learns the region wise image features and performs a classification job. These classification results are later used in the encoder downstream systems for finding the optimal coding units in each of the tree blocks, and subsequently reduce the number of prediction modes. For our model training, we gathered a new data-set which includes diverse images for the better generalization of our results. The experimental results show that our proposed learning based algorithm reduces the encoder time up to 66.15 % with a minimal Bjontegaard Delta Bit Rate (BD-BR) loss of 1.34 % over the state-of-the-art machine learning approaches. Furthermore our method also reduces the mode selection by 45.91 % with respect to the HEVC baseline.
Keywords
CNN, Region of Interest (ROI), CU partition, Angular mode selection, Softmax classifier
Disciplines
Electrical and Computer Engineering | Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Kuanar, Shiba Prasad, "Deep Learning based Fast Mode Decision in HEVC Intra Prediction using Region Wise Feature Classification" (2018). Electrical Engineering Dissertations. 379.
https://mavmatrix.uta.edu/electricaleng_dissertations/379
Comments
Degree granted by The University of Texas at Arlington