Hardware Acceleration of Most Apparent Distortion Image Quality Assessment Algorithm on FPGA Using OpenCL

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/ThesisMasters Thesis Electrical Engineering 201

A real-time near infrared image acquisition system based on image quality assessment

This paper presents a real-time image acquisition system with an improved image quality assessment module to acquire high-quality near infrared (NIR) images. Thermal imaging plays a vital role in a wide range of medical and military applications. The demand for high-throughput image acquisition and image processing has continuously increased especially for critical medical and military purposes where executions under real-time constraints are required. This work implements an NIR image quality assessment module, which utilizes improved two-dimensional entropy and mask-based edge detection algorithms. The effectiveness of the proposed image quality assessment algorithms is demonstrated through the implementation of a complete finger-vein biometric system. The proposed model is implemented as an embedded system on a field programmable gate array prototyping platform. By including the image quality assessment module, the proposed system is able to achieve a recognition accuracy of 0.87 % equal error rate, and can handle real-time processing at 15 frames/s (live video rate). This is achieved through hardware acceleration of the proposed image quality assessment algorithms via a novel streaming architecture