abstract: The information era has brought about many technological advancements in the past
few decades, and that has led to an exponential increase in the creation of digital images and
videos. Constantly, all digital images go through some image processing algorithm for
various reasons like compression, transmission, storage, etc. There is data loss during this
process which leaves us with a degraded image. Hence, to ensure minimal degradation of
images, the requirement for quality assessment has become mandatory. Image Quality
Assessment (IQA) has been researched and developed over the last several decades to
predict the quality score in a manner that agrees with human judgments of quality. Modern
image quality assessment (IQA) algorithms are quite effective at prediction accuracy, and
their development has not focused on improving computational performance. The existing
serial implementation requires a relatively large run-time on the order of seconds for a single
frame. Hardware acceleration using Field programmable gate arrays (FPGAs) provides
reconfigurable computing fabric that can be tailored for a broad range of applications.
Usually, programming FPGAs has required expertise in hardware descriptive languages
(HDLs) or high-level synthesis (HLS) tool. OpenCL is an open standard for cross-platform,
parallel programming of heterogeneous systems along with Altera OpenCL SDK, enabling
developers to use FPGA's potential without extensive hardware knowledge. Hence, this
thesis focuses on accelerating the computationally intensive part of the most apparent
distortion (MAD) algorithm on FPGA using OpenCL. The results are compared with CPU
implementation to evaluate performance and efficiency gains. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2017
| Identifer | oai:union.ndltd.org:asu.edu/item:44286 |
| Date | January 2017 |
| Contributors | Gunavelu Mohan, Aswin (Author), Sohoni, Sohum (Advisor), Ren, Fengbo (Advisor), Seo, Jae-sun (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 66 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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