Objective Quality Assessment and Optimization for High Dynamic Range Image Tone Mapping

Tone mapping operators aim to compress high dynamic range (HDR) images to low dynamic range ones so as to visualize HDR images on standard displays. Most existing works were demonstrated on specific examples without being thoroughly tested on well-established and subject-validated image quality assessment models. A recent tone mapped image quality index (TMQI) made the first attempt on objective quality assessment of tone mapped images. TMQI consists of two fundamental building blocks: structural fidelity and statistical naturalness. In this thesis, we propose an enhanced tone mapped image quality index (eTMQI) by 1) constructing an improved nonlinear mapping function to better account for the local contrast visibility of HDR images and 2) developing an image dependent statistical naturalness model to quantify the unnaturalness of tone mapped images based on a subjective study. Experiments show that the modified structural fidelity and statistical naturalness terms in eTMQI better correlate with subjective quality evaluations. Furthermore, we propose an iterative optimization algorithm for tone mapping. The advantages of this algorithm are twofold: 1) eTMQI and TMQI can be compared in a more straightforward way; 2) better quality tone mapped images can be automatically generated by using eTMQI as the optimization goal. Numerical and subjective experiments demonstrate that eTMQI is a superior objective quality assessment metric for tone mapped images and consistently outperforms TMQI

Blind Image Quality Assessment: Exploiting New Evaluation and Design Methodologies

The great content diversity of real-world digital images poses a grand challenge to automatically and accurately assess their perceptual quality in a timely manner. In this thesis, we focus on blind image quality assessment (BIQA), which predicts image quality with no access to its pristine quality counterpart. We first establish a large-scale IQA database---the Waterloo Exploration Database. It contains 4,744 pristine natural and 94,880 distorted images, the largest in the IQA field. Instead of collecting subjective opinions for each image, which is extremely difficult, we present three test criteria for evaluating objective BIQA models: pristine/distorted image discriminability test (D-test), listwise ranking consistency test (L-test), and pairwise preference consistency test (P-test). Moreover, we propose a general psychophysical methodology, which we name the group MAximum Differentiation (gMAD) competition method, for comparing computational models of perceptually discriminable quantities. We apply gMAD to the field of IQA and compare 16 objective IQA models of diverse properties. Careful investigations of selected stimuli shed light on how to improve existing models and how to develop next-generation IQA models. The gMAD framework is extensible, allowing future IQA models to be added to the competition. We explore novel approaches for BIQA from two different perspectives. First, we show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIPs) can be obtained automatically at low cost. We extend a pairwise learning-to-rank (L2R) algorithm to learn BIQA models from millions of DIPs. Second, we propose a multi-task deep neural network for BIQA. It consists of two sub-networks---a distortion identification network and a quality prediction network---sharing the early layers. In the first stage, we train the distortion identification sub-network, for which large-scale training samples are readily available. In the second stage, starting from the pre-trained early layers and the outputs of the first sub-network, we train the quality prediction sub-network using a variant of stochastic gradient descent. Extensive experiments on four benchmark IQA databases demonstrate the proposed two approaches outperform state-of-the-art BIQA models. The robustness of learned models is also significantly improved as confirmed by the gMAD competition methodology