HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos

We present a no-reference video quality model and algorithm that delivers standout performance for High Dynamic Range (HDR) videos, which we call HDR-ChipQA. HDR videos represent wider ranges of luminances, details, and colors than Standard Dynamic Range (SDR) videos. The growing adoption of HDR in massively scaled video networks has driven the need for video quality assessment (VQA) algorithms that better account for distortions on HDR content. In particular, standard VQA models may fail to capture conspicuous distortions at the extreme ends of the dynamic range, because the features that drive them may be dominated by distortions {that pervade the mid-ranges of the signal}. We introduce a new approach whereby a local expansive nonlinearity emphasizes distortions occurring at the higher and lower ends of the {local} luma range, allowing for the definition of additional quality-aware features that are computed along a separate path. These features are not HDR-specific, and also improve VQA on SDR video contents, albeit to a reduced degree. We show that this preprocessing step significantly boosts the power of distortion-sensitive natural video statistics (NVS) features when used to predict the quality of HDR content. In similar manner, we separately compute novel wide-gamut color features using the same nonlinear processing steps. We have found that our model significantly outperforms SDR VQA algorithms on the only publicly available, comprehensive HDR database, while also attaining state-of-the-art performance on SDR content

Evaluation of the color image and video processing chain and visual quality management for consumer systems

With the advent of novel digital display technologies, color processing is increasingly becoming a key aspect in consumer video applications. Today’s state-of-the-art displays require sophisticated color and image reproduction techniques in order to achieve larger screen size, higher luminance and higher resolution than ever before. However, from color science perspective, there are clearly opportunities for improvement in the color reproduction capabilities of various emerging and conventional display technologies. This research seeks to identify potential areas for improvement in color processing in a video processing chain. As part of this research, various processes involved in a typical video processing chain in consumer video applications were reviewed. Several published color and contrast enhancement algorithms were evaluated, and a novel algorithm was developed to enhance color and contrast in images and videos in an effective and coordinated manner. Further, a psychophysical technique was developed and implemented for performing visual evaluation of color image and consumer video quality. Based on the performance analysis and visual experiments involving various algorithms, guidelines were proposed for the development of an effective color and contrast enhancement method for images and video applications. It is hoped that the knowledge gained from this research will help build a better understanding of color processing and color quality management methods in consumer video

Brilliance, contrast, colorfulness, and the perceived volume of device color gamut

With the advent of digital video and cinema media technologies, much more is possible in achieving brighter and more vibrant colors, colors that transcend our experience. The challenge is in the realization of these possibilities in an industry rooted in 1950s technology where color gamut is represented with little or no insight into the way an observer perceives color as a complex mixture of the observer’s intentions, desires, and interests. By today’s standards, five perceptual attributes – brightness, lightness, colorfulness, chroma, and hue - are believed to be required for a complete specification. As a compelling case for such a representation, a display system is demonstrated that is capable of displaying color beyond the realm of object color, perceptually even beyond the spectrum locus of pure color. All this begs the question: Just what is meant by perceptual gamut? To this end, the attributes of perceptual gamut are identified through psychometric testing and the color appearance models CIELAB and CIECAM02. Then, by way of demonstration, these attributes were manipulated to test their application in wide gamut displays. In concert with these perceptual attributes and their manipulation, Ralph M. Evans’ concept of brilliance as an attribute of perception that extends beyond the realm of everyday experience, and the theoretical studies of brilliance by Y. Nayatani, a method was developed for producing brighter, more colorful colors and deeper, darker colors with the aim of preserving object color perception – flesh tones in particular. The method was successfully demonstrated and tested in real images using psychophysical methods in the very real, practical application of expanding the gamut of sRGB into an emulation of the wide gamut, xvYCC encoding

Full Reference Objective Quality Assessment for Reconstructed Background Images

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated Database: https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing (Email for permissions at: ashrotreasuedu

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Evaluation and optimal design of spectral sensitivities for digital color imaging

The quality of an image captured by color imaging system primarily depends on three factors: sensor spectral sensitivity, illumination and scene. While illumination is very important to be known, the sensitivity characteristics is critical to the success of imaging applications, and is necessary to be optimally designed under practical constraints. The ultimate image quality is judged subjectively by human visual system. This dissertation addresses the evaluation and optimal design of spectral sensitivity functions for digital color imaging devices. Color imaging fundamentals and device characterization are discussed in the first place. For the evaluation of spectral sensitivity functions, this dissertation concentrates on the consideration of imaging noise characteristics. Both signal-independent and signal-dependent noises form an imaging noise model and noises will be propagated while signal is processed. A new colorimetric quality metric, unified measure of goodness (UMG), which addresses color accuracy and noise performance simultaneously, is introduced and compared with other available quality metrics. Through comparison, UMG is designated as a primary evaluation metric. On the optimal design of spectral sensitivity functions, three generic approaches, optimization through enumeration evaluation, optimization of parameterized functions, and optimization of additional channel, are analyzed in the case of the filter fabrication process is unknown. Otherwise a hierarchical design approach is introduced, which emphasizes the use of the primary metric but the initial optimization results are refined through the application of multiple secondary metrics. Finally the validity of UMG as a primary metric and the hierarchical approach are experimentally tested and verified

Subjective Assessment of Image Compression Artefacts on Stereoscopic Display

Image and video quality are important to depict any pictorial information vividly and correctly. With the advancement of technology, we can produce high-quality images and can display those in advanced high-resolution displays. But as high-quality images continue to increase in size, transmitting these exceeds the limited bandwidth of display links. To cope, we need to compress the images but desire that the user cannot perceive any difference between the compressed and uncompressed images. In my thesis, psychophysical experiments with a flicker paradigm were undertaken to do a subjective assessment of the visibility of compression artefacts of two sets of images with two codecs viewed on a stereoscopic display. For one set of images the result shows that artefacts can be silenced in some stereo images relative to 2D while testing with the other set of images was inconclusive. This thesis documented evidence for silencing of artefacts in 3D displays. Other differences between stereoscopic and 2D presentation can be predicted but were not observed here (perhaps due to floor effects). Further large-scale subjective assessment with challenging images may help to get a concrete conclusion