Cross Dynamic Range And Cross Resolution Objective Image Quality Assessment With Applications

In recent years, image and video signals have become an indispensable part of human life. There has been an increasing demand for high quality image and video products and services. To monitor, maintain and enhance image and video quality objective image and video quality assessment tools play crucial roles in a wide range of applications throughout the field of image and video processing, including image and video acquisition, communication, interpolation, retrieval, and displaying. A number of objective image and video quality measures have been introduced in the last decades such as mean square error (MSE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM). However, they are not applicable when the dynamic range or spatial resolution of images being compared is different from that of the corresponding reference images. In this thesis, we aim to tackle these two main problems in the field of image quality assessment. Tone mapping operators (TMOs) that convert high dynamic range (HDR) to low dynamic range (LDR) images provide practically useful tools for the visualization of HDR images on standard LDR displays. Most TMOs have been designed in the absence of a well-established and subject-validated image quality assessment (IQA) model, without which fair comparisons and further improvement are difficult. We propose an objective quality assessment algorithm for tone-mapped images using HDR images as references by combining 1) a multi-scale signal fidelity measure based on a modified structural similarity (SSIM) index; and 2) a naturalness measure based on intensity statistics of natural images. To evaluate the proposed Tone-Mapped image Quality Index (TMQI), its performance in several applications and optimization problems is provided. Specifically, the main component of TMQI known as structural fidelity is modified and adopted to enhance the visualization of HDR medical images on standard displays. Moreover, a substantially different approach to design TMOs is presented, where instead of using any pre-defined systematic computational structure (such as image transformation or contrast/edge enhancement) for tone-mapping, we navigate in the space of all LDR images, searching for the image that maximizes structural fidelity or TMQI. There has been an increasing number of image interpolation and image super-resolution (SR) algorithms proposed recently to create images with higher spatial resolution from low-resolution (LR) images. However, the evaluation of such SR and interpolation algorithms is cumbersome. Most existing image quality measures are not applicable because LR and resultant high resolution (HR) images have different spatial resolutions. We make one of the first attempts to develop objective quality assessment methods to compare LR and HR images. Our method adopts a framework based on natural scene statistics (NSS) where image quality degradation is gauged by the deviation of its statistical features from NSS models trained upon high quality natural images. In particular, we extract frequency energy falloff, dominant orientation and spatial continuity statistics from natural images and build statistical models to describe such statistics. These models are then used to measure statistical naturalness of interpolated images. We carried out subjective tests to validate our approach, which also demonstrates promising results. The performance of the proposed measure is further evaluated when applied to parameter tuning in image interpolation algorithms

High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization

In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perceptionComment: 4 pages, 5 figures, 2 tables, to be published at IEEE Visual Communications and Image Processing (VCIP) 201

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Image enhancement from a stabilised video sequence

The aim of video stabilisation is to create a new video sequence where the motions (i.e. rotations, translations) and scale differences between frames (or parts of a frame) have effectively been removed. These stabilisation effects can be obtained via digital video processing techniques which use the information extracted from the video sequence itself, with no need for additional hardware or knowledge about camera physical motion. A video sequence usually contains a large overlap between successive frames, and regions of the same scene are sampled at different positions. In this paper, this multiple sampling is combined to achieve images with a higher spatial resolution. Higher resolution imagery play an important role in assisting in the identification of people, vehicles, structures or objects of interest captured by surveillance cameras or by video cameras used in face recognition, traffic monitoring, traffic law reinforcement, driver assistance and automatic vehicle guidance systems