Browsing by Subject "Imaging systems--Image quality"

Now showing 1 - 4 of 4

Electric-field-induced second harmonic microscopy
(2004) Wu, Kui; Downer, Michael Coffin.
Image communication system design based on the structural similarity index
(2007) Channappayya, Sumohana S., 1977-; Bovik, Alan C. (Alan Conrad), 1958-; Heath, Robert W., Jr, 1973-
The amount of digital image and video content being generated and shared has grown explosively in the recent past. The primary goal of image and video communication systems is to achieve the best possible visual quality at a given rate constraint and channel conditions. In this dissertation, the focus is limited to image communication systems. In order to optimize the components of the communication system to maximize perceptual quality, it is important to use a good measure of quality. Even though this fact has been long recognized, the mean squared error (MSE), which is not the best measure of perceptual quality, has been a popular choice in the design of various components of an image communication system. Recent developments in the field of image quality assessment (IQA) have resulted in the development of powerful new algorithms. A few of these new algorithms include the structural similarity (SSIM) index, the visual information fidelity (VIF) criterion, and the visual signal to noise ratio (VSNR). The SSIM index is considered in this dissertation. I demonstrate that optimizing image processing algorithms for the SSIM index does indeed result in an improvement in the perceptual quality of the processed images. All the comparisons in this thesis are made against appropriate MSE-optimal equivalents. First, an SSIM-optimal linear estimator is derived and applied to the problem of image denoising. An algorithm for SSIM-optimal linear equalization is developed and applied to the problem of image restoration. Followed by the development of the linear solution, I addressed the problem of SSIM-optimal soft thresholding which is a nonlinear technique. The estimation, equalization, and soft-thresholding results all show a gain in the visual quality compared to their MSE-optimal counterparts. These solutions are typically used at the receiver of an image communication system. On the transmitter side of the system, bounds on the SSIM index as a function of the rate allocated to a uniform quantizer are derived.
Image quality assessment using natural scene statistics
(2004) Sheikh, Hamid Rahim; Bovik, Alan C.; Cormack, Lawrence K.
Measurement of image quality is crucial for designing image processing systems that could potentially degrade visual quality. Such measurements allow developers to optimize designs to deliver maximum quality while minimizing system cost. This dissertation is about automatic algorithms for quality assessment of digital images. Traditionally, researchers have equated image quality with image fi- delity, or the closeness of a distorted image to a ‘reference’ image that is assumed to have perfect quality. This closeness is typically measured by modeling the human visual system, or by using different mathematical criteria for signal similarity. In this dissertation, I approach the problem from a novel direction. I claim that quality assessment algorithms deal only with images and videos that are meant for human consumption, and that these signals are almost exclusively images and videos of the visual environment. Image distortions make these so-called natural scenes look ‘unnatural’. I claim that this departure from ‘expected’ characteristics could be quantified for predicting visual quality. I present a novel information-theoretic approach to image quality assessment using statistical models for natural scenes. I approach the quality assessment problem as an information fidelity problem, in which the distortion process is viewed as a channel that limits the flow of information from a source of natural images to the receiver (the brain). I show that quality of a test image is strongly related to the amount of statistical information about the reference image that is present in the test image. I also explore image quality assessment in the absence of the reference, and present a novel method for blindly quantifying the quality of images compressed by wavelet based compression algorithms. I show that images are rendered unnatural by the quantization process during lossy compression, and that this unnaturalness could be quantified blindly for predicting visual quality. I test and validate the performance of the algorithms proposed in this dissertation through an extensive study in which ground truth data was obtained from many human subjects. I show that the methods presented can accurately predict visual quality, and that they outperform current state-ofthe-art methods in my simulations.
Video quality assessment based on motion models
(2008-08) Seshadrinathan, Kalpana, 1980-; Bovik, Alan C. (Alan Conrad), 1958-
A large amount of digital visual data is being distributed and communicated globally and the question of video quality control becomes a central concern. Unlike many signal processing applications, the intended receiver of video signals is nearly always the human eye. Video quality assessment algorithms must attempt to assess perceptual degradations in videos. My dissertation focuses on full reference methods of image and video quality assessment, where the availability of a perfect or pristine reference image/video is assumed. A large body of research on image quality assessment has focused on models of the human visual system. The premise behind such metrics is to process visual data by simulating the visual pathway of the eye-brain system. Recent approaches to image quality assessment, the structural similarity index and information theoretic models, avoid explicit modeling of visual mechanisms and use statistical properties derived from the images to formulate measurements of image quality. I show that the structure measurement in structural similarity is equivalent to contrast masking models that form a critical component of many vision based methods. I also show the equivalence of the structural and the information theoretic metrics under certain assumptions on the statistical distribution of the reference and distorted images. Videos contain many artifacts that are specific to motion and are largely temporal. Motion information plays a key role in visual perception of video signals. I develop a general, spatio-spectrally localized multi-scale framework for evaluating dynamic video fidelity that integrates both spatial and temporal aspects of distortion assessment. Video quality is evaluated in space and time by evaluating motion quality along computed motion trajectories. Using this framework, I develop a full-reference video quality assessment algorithm known as the MOtion-based Video Integrity Evaluation index, or MOVIE index. Lastly, and significantly, I conducted a large-scale subjective study on a database of videos distorted by present generation video processing and communication technology. The database contains 150 distorted videos obtained from 10 naturalistic reference videos and each video was evaluated by 38 human subjects in the study. I study the performance of leading, publicly available objective video quality assessment algorithms on this database.