| Visual signals, including images, videos, etc., are affected by a wide variety of distortions during acquisition, compression, storage, processing, transmission, and reproduction processes, which result in perceptual quality degradation. As a result, perceptual quality assessment plays a very important role in today's visual signal processing and communication systems. In this thesis, quality assessment algorithms for evaluating the visual signal perceptual quality, as well as the applications on visual signal processing and communications, are investigated. The work consists of five parts as briefly summarized below.;The first part focuses on the full-reference (FR) image quality assessment. The properties of the human visual system (HVS) are firstly investigated. Specifically, the visual horizontal effect (HE) and saliency properties over the structural distortions are modelled and incorporated into the structure similarity index (SSIM). Experimental results show significantly improved performance in matching the subjective ratings. Inspired by the developed FR image metric, a perceptual image compression scheme is developed, where the adaptive block-based super-resolution directed down-sampling is proposed. Experimental results demonstrated that the proposed image compression scheme can produce higher quality images in terms of both objective and subjective qualities, compared with the existing methods.;The second part concerns the FR video quality assessment. The adaptive block-size transform (ABT) based just-noticeable difference (JND) for visual signals is investigated by considering the HVS characteristics, e.g., spatio-temporal contrast sensitivity function (CSF), eye movement, texture masking, spatial coherence, temporal consistency, properties of different block-size transforms, etc. It is verified that the developed ABT based JND can more accurately depict the HVS property, compared with the state-of-the-art JND models. The ABT based JND is thereby utilized to develop a simple perceptual quality metric for visual signals. Validations on the image and video subjective quality databases proved its effectiveness. As a result, the developed perceptual quality metric is employed for perceptual video coding, which can deliver video sequences of higher perceptual quality at the same bit-rates.;The third part discusses the reduced-reference (RR) image quality assessment, which is developed by statistically modelling the coefficient distribution in the reorganized discrete cosine transform (RDCT) domain. The proposed RR metric exploits the identical statistical nature of the adjacent DCT coefficients, the mutual information (MI) relationship between adjacent RDCT coefficients, and the image energy distribution among different frequency components. Experimental results demonstrate that the proposed metric outperforms the representative RR image quality metrics, and even the FR quality metric, i.e., peak signal to noise ratio (PSNR). Furthermore, the extracted RR features can be easily encoded and embedded into the distorted images for quality monitoring during image communications.;The fourth part investigates the RR video quality assessment. The RR features are extracted to exploit the spatial information loss and the temporal statistical characteristics of the inter-frame histogram. Evaluations on the video subjective quality databases demonstrate that the proposed method outperforms the representative RR video quality metrics, and even the FR metrics, such as PSNR, SSIM in matching the subjective ratings. Furthermore, only a small number of RR features is required to represent the original video sequence (each frame requires only 1 and 3 parameters to depict the spatial and temporal characteristics, respectively). By considering the computational complexity and the bit-rates for extracting and representing the RR features, the proposed RR quality metric can be utilized for quality monitoring during video transmissions, where the RR features for perceptual quality analysis can be easily embedded into the videos or transmitted through an ancillary data channel.;The aforementioned perceptual quality metrics focus on the traditional distortions, such as JPEG image compression noise, H.264 video compression noise, and so on. In the last part, we investigate the distortions introduced during the image and video retargeting process. Nowadays, with the development of the consumer electronics, more and more visual signals have to communicate between different display devices of different resolutions. The retargeting algorithm is employed to adapt a source image of one resolution to be displayed in a device of a different resolution, which may introduce distortions during the retargeting process. We investigate the subjective responses on the perceptual qualities of the retargeted images, and discuss the subjective results from three perspectives, i.e., retargeting scales, retargeting methods, and source image content attributes. An image retargeting subjective quality database is built by performing a large-scale subjective study of image retargeting quality on a collection of retargeted images. Based on the built database, several representative quality metrics for retargeted images are evaluated and discussed. |
