Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes

abstract: Digital imaging and image processing technologies have revolutionized the way in which we capture, store, receive, view, utilize, and share images. In image-based applications, through different processing stages (e.g., acquisition, compression, and transmission), images are subjected to different types of distortions which degrade their visual quality. Image Quality Assessment (IQA) attempts to use computational models to automatically evaluate and estimate the image quality in accordance with subjective evaluations. Moreover, with the fast development of computer vision techniques, it is important in practice to extract and understand the information contained in blurred images or regions. The work in this dissertation focuses on reduced-reference visual quality assessment of images and textures, as well as perceptual-based spatially-varying blur detection. A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The proposed method requires a very small number of reduced-reference (RR) features. Extensive experiments performed on different benchmark databases demonstrate that the proposed RRIQA method, delivers highly competitive performance as compared with the state-of-the-art RRIQA models for both natural and texture images. In the context of texture, the effect of texture granularity on the quality of synthesized textures is studied. Moreover, two RR objective visual quality assessment methods that quantify the perceived quality of synthesized textures are proposed. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metrics outperforms full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures. Last but not least, an effective approach to address the spatially-varying blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings is proposed. The evaluations of the proposed approach on a diverse sets of blurry images with different blur types, levels, and content demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment

Perceptual video quality assessment models are either frame-based or video-based, i.e., they apply spatiotemporal filtering or motion estimation to capture temporal video distortions. Despite their good performance on video quality databases, video-based approaches are time-consuming and harder to efficiently deploy. To balance between high performance and computational efficiency, Netflix developed the Video Multi-method Assessment Fusion (VMAF) framework, which integrates multiple quality-aware features to predict video quality. Nevertheless, this fusion framework does not fully exploit temporal video quality measurements which are relevant to temporal video distortions. To this end, we propose two improvements to the VMAF framework: SpatioTemporal VMAF and Ensemble VMAF. Both algorithms exploit efficient temporal video features which are fed into a single or multiple regression models. To train our models, we designed a large subjective database and evaluated the proposed models against state-of-the-art approaches. The compared algorithms will be made available as part of the open source package in https://github.com/Netflix/vmaf

Performance-analysis-based Acceleration of Image Quality Assessment

Algorithms for image/video quality assessment (QA) aim to predict the qualitiesof images in a manner that agrees with subjective quality ratings. Over the lastseveral decades, the major impetus in QA research has focused on improving predictiveperformance; very few studies have focused on analyzing and improving theruntime performance of QA algorithms. Modern algorithms of image/video qualityassessment commonly employed two stages: (1) a local frequency-based decomposition, and (2) block-based statistical comparisons between the frequency coefficients of the reference and distorted images. These two stages constitute the bulk of the computation and runtime required for QA. This research thesis presents a performance analysis of and techniques for accelerating these stages. We also specifically analyze and accelerate one representative QA algorithm, Most Apparent Distortion (MAD), which was developed by Eric Larson and Damon Chandler in 2010 [1]. We identify the bottlenecks from the above-mentioned stages, and we present methods of acceleration using generalized integral image, inline expansion, a GPGPU implementation, and other code modifications. We show how a combination of these approaches can yield a speedup of 47x.The content of the report is divided into five different chapters. In Chapter 1,we present a general overview of QA algorithms, current work on improving the computational performance and execution time of QA algorithms, and an introduction toour work. In Chapter 2, we describe MAD algorithm, the first performance analysis,and the systems used to test the performance. In Chapter 3, we present generalizedintegral image and inline expansion techniques. In this chapter, we also providethe results of each technique in terms of speeding up running time. Chapter 4 providesGPGPU and some other code optimization techniques with the timing results.Finally, the conclusion are proposed in the Chapter 5 to summarize the report.Electrical Engineerin

A reduced reference video quality assessment method for provision as a service over SDN/NFV-enabled networks

139 p.The proliferation of multimedia applications and services has generarted a noteworthy upsurge in network traffic regarding video content and has created the need for trustworthy service quality assessment methods. Currently, predominent position among the technological trends in telecommunication networkds are Network Function Virtualization (NFV), Software Defined Networking (SDN) and 5G mobile networks equipped with small cells. Additionally Video Quality Assessment (VQA) methods are a very useful tool for both content providers and network operators, to understand of how users perceive quality and this study the feasibility of potential services and adapt the network available resources to satisfy the user requirements