WAVELET AND SINE BASED ANALYSIS OF PRINT QUALITY EVALUATIONS

Recent advances in imaging technology have resulted in a proliferation of images across different media. Before it reaches the end user, these signals undergo several transformations, which may introduce defects/artifacts that affect the perceived image quality. In order to design and evaluate these imaging systems, perceived image quality must be measured. This work focuses on analysis of print image defects and characterization of printer artifacts such as banding and graininess by using a human visual system (HVS) based framework. Specifically the work addresses the prediction of visibility of print defects (banding and graininess) by representing the print defects in terms of the orthogonal wavelet and sinusoidal basis functions and combining the detection probabilities of each basis functions to predict the response of the human visual system (HVS). The detection probabilities for basis function components and the simulated print defects are obtained from separate subjective tests. The prediction performance from both the wavelet based and sine based approaches is compared with the subjective testing results .The wavelet based prediction performs better than the sinusoidal based approach and can be a useful technique in developing measures and methods for print quality evaluations based on HVS

Perceptual Fidelity for Digital Color Imagery

The problem of measuring the fidelity of digital color images in a manner that corresponds to human perceptual assessments is addressed. Experiments are performed to validate human visual system (HVS) models, which provide access to a \u27perceptual space\u27 in which visual distortions may be measured, and then a model is proposed for assessing the perceptual fidelity of digital color image. Color Mach bands are produced in the first experiment, demonstrating that, as in the brightness channel, low spatial frequency attenuation occurs in the chromatic channels of the HVS. In the second experiment, a correlation between the chromatic channels of the HVS model and color discrimination axes of color blind observers is demonstrated. Removing variation from one of the chromatic channels of a natural image produces a color-distorted image which the color blind subjects cannot distinguish from the original. Removing variation from the other chromatic channel produces an image that appears colorful to normally-sighted observers, but monochrome to the color blind observers. The third experiment shows that a Gabor filter-based HVS model produces illusory contours in several illusory contour stimuli. These results provide a unique validation of multiple-channel HVS models which process the image in multiple spatial frequency bands that are tuned to match measured sensitivities of neurons in the primary visual cortex of cats and monkeys. Finally, the multiple-channel processing used in the illusory contour experiment is combined with the color vision model from the first two experiments to produce a multiple-channel, color HVS model for measuring perceptual fidelity of color images. A demonstration of the model shows that the structure of the new model is correct. However, inaccurate parameter values for the multiple-channel processing of the chromatic channels cause over-prediction of visible differences in these channels

Recent Progress in the Development of INCITS W1.1, Appearance-Based Image Quality Standards for Printers

In September 2000, INCITS W1 (the U.S. representative of ISO/IEC JTC1/SC28, the standardization committee for office equipment) was chartered to develop an appearance-based image quality standard.(J),(2) The resulting W1.1 project is based on a proposal(4) that perceived image quality can be described by a small set of broad-based attributes. There are currently five ad hoc teams, each working towards the development of standards for evaluation of perceptual image quality of color printers for one or more of these image quality attributes. This paper summarizes the work in progress

Perceptual models for high-refresh-rate rendering

Rendering realistic images requires substantial computational power. With new high-refresh-rate displays as well as the renaissance of virtual reality (VR) and augmented reality (AR), one cannot expect that GPU performance will scale fast enough to meet the requirements of immersive photo-realistic rendering with current rendering techniques. In this dissertation, I follow the dual of the well-known computer vision approach: vision is inverse graphics: to improve graphical algorithms, I consider the operation of the human visual system. I propose to model and exploit the limitations of the visual system in the context of novel high-refresh-rate displays; specifically, I focus on spatio-temporal perception, a topic that has received remarkably less attention than spatial-only perception so far. I present three main contributions. First, I demonstrate the validity of the perceptual approach by presenting a conceptually simple rendering technique motivated by our eyes' limited sensitivity to high spatio-temporal change which reduces the rendering load and transmission requirement of current-generation VR headsets without introducing perceivable visual artefacts. Second, I present two visual models related to motion perception: (a) a metric for detecting flicker; and (b) a comprehensive visual model to predict perceived motion quality on monitors with arbitrary refresh rates and monitor resolutions. Third, I propose an adaptive rendering algorithm that utilises the proposed models. All algorithms operate on physical colorimetric units (instead of display-referenced pixel values), for which I provide the appropriate display measurements and models. All proposed algorithms and visual models are calibrated and validated with psychophysical experiments

PIM: Video Coding using Perceptual Importance Maps

Human perception is at the core of lossy video compression, with numerous approaches developed for perceptual quality assessment and improvement over the past two decades. In the determination of perceptual quality, different spatio-temporal regions of the video differ in their relative importance to the human viewer. However, since it is challenging to infer or even collect such fine-grained information, it is often not used during compression beyond low-level heuristics. We present a framework which facilitates research into fine-grained subjective importance in compressed videos, which we then utilize to improve the rate-distortion performance of an existing video codec (x264). The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions. We demonstrate via a subjective study that encoding the videos in our dataset while taking into account the importance maps leads to higher perceptual quality at the same bitrate, with the videos encoded with importance maps preferred $1.8 \times$ over the baseline videos. Similarly, we show that for the 18 videos in test set, the importance maps predicted by our model lead to higher perceptual quality videos, $2 \times$ preferred over the baseline at the same bitrate