Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Cataloged from PDF version of article.Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (20) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics. (C) 2011 Elsevier Ltd. All rights reserved

Visibility metrics and their applications in visually lossless image compression

Visibility metrics are image metrics that predict the probability that a human observer can detect differences between a pair of images. These metrics can provide localized information in the form of visibility maps, in which each value represents a probability of detection. An important application of the visibility metric is visually lossless image compression that aims at compressing a given image to the lowest fraction of bit per pixel while keeping the compression artifacts invisible at the same time. In previous works, most visibility metrics were modeled based on largely simplified assumptions and mathematical models of human visual systems. This approach generally fits well into experimental data measured with simple stimuli, such as Gabor patches. However, it cannot predict complex non-linear effects, such as contrast masking in natural images, particularly well. To predict visibility of image differences accurately, we collected the largest visibility dataset under fixed viewing conditions for calibrating existing visibility metrics and proposed a deep neural network-based visibility metric. We demonstrated in our experiments that the deep neural network-based visibility metric significantly outperformed existing visibility metrics. However, the deep neural network-based visibility metric cannot predict visibility under varying viewing conditions, such as display brightness and viewing distances that have great impacts on the visibility of distortions. To extend the deep neural network-based visibility metric to varying viewing conditions, we collected the largest visibility dataset under varying display brightness and viewing distances. We proposed incorporating white-box modules, in other words, luminance masking and viewing distance adaptation, into the black-box deep neural network, and we found that the combination of white-box modules and black-box deep neural networks could generalize our proposed visibility metric to varying viewing conditions. To demonstrate the application of our proposed deep neural network-based visibility metric to visually lossless image compression, we collected the visually lossless image compression dataset under fixed viewing conditions and significantly improved the deep neural network-based visibility metric's accuracy of predicting visually lossless image compression threshold by pre-training the visibility metric with a synthetic dataset generated by the state-of-the-art white-box visibility metric---HDR-VDP \cite{Mantiuk2011}. In a large-scale study of 1000 images, we found that with our improved visibility metric, we can save around 60\% to 70\% bits for visually lossless image compression encoding as compared to the default visually lossless quality level of 90. Because predicting image visibility and predicting image quality are closely related research topics, we also proposed a trained perceptually uniform transform for high dynamic range images and videos quality assessments by training a perceptual encoding function on a set of subjective quality assessment datasets. We have shown that when combining the trained perceptual encoding function with standard dynamic range image quality metrics, such as peak-signal-noise-ratio (PSNR), better performance was achieved compared to the untrained version

Perspectives on panoramic photography

Digital imaging brings a new set of possibilities to photography. For example, little pictures can be assembled to form a large panorama, and digital cameras are trying to mimic the human visual system to produce better pictures. This manuscript aims at developing the algorithms required to stitch a set of pictures together to obtain a bigger and better image. This thesis explores three important topics of panoramic photography: The alignment of images, the matching of the colours, and the rendering of the resulting panorama. In addition, one chapter is devoted to 3D and constrained estimation. Aligning pictures can be difficult when the scene changes while taking the photographs. A method is proposed to model these changes —or outliers— that appear in image pairs, by computing the outlier distribution from the image histograms and handling the image-to-image correspondence problem as a mixture of inliers versus outliers. Compared to the standard methods, this approach uses the information contained in the image in a better way, and leads to a more reliable result. Digital cameras aim at reproducing the adaptation capabilities of the human eye in capturing the colours of a scene. As a consequence, there is often a large colour mismatch between two pictures. This work exposes a novel way of correcting for colour mismatches by modelling the transformation introduced by the camera, and reversing it to get consistent colours. Finally, this manuscript proposes a method to render high dynamic range images that contain very bright as well as very dark regions. To reproduce this kind of pictures the contrast has to be reduced in order to match the maximum contrast displayable on a screen or on paper. This last method, which is based on a complex model of the human visual system, reduces the contrast of the image while maintaining the little details visible the scene

From pairwise comparisons and rating to a unified quality scale.

The goal of psychometric scaling is the quantification of perceptual experiences, understanding the relationship between an external stimulus, the internal representation and the response. In this paper, we propose a probabilistic framework to fuse the outcome of different psychophysical experimental protocols, namely rating and pairwise comparisons experiments. Such a method can be used for merging existing datasets of subjective nature and for experiments in which both measurements are collected. We analyze and compare the outcomes of both types of experimental protocols in terms of time and accuracy in a set of simulations and experiments with benchmark and real-world image quality assessment datasets, showing the necessity of scaling and the advantages of each protocol and mixing. Although most of our examples focus on image quality assessment, our findings generalize to any other subjective quality-of-experience task.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement n◦ 725253–EyeCode), from EPSRC research grant EP/P007902/1 and from a Science Foundation Ireland (SFI) research grant under the Grant Number 15/RP/2776. Marıa Pérez-Ortiz did part of this work while at the University of Cambridge and University College London (under MURI grant EPSRC 542892)

Objective and subjective assessment of perceptual factors in HDR content processing

The development of the display and camera technology makes high dynamic range (HDR) image become more and more popular. High dynamic range image give us pleasant image which has more details that makes high dynamic range image has good quality. This paper shows us the some important techniques in HDR images. And it also presents the work the author did. The paper is formed of three parts. The first part is an introduction of HDR image. From this part we can know why HDR image has good quality

Optimization and improvements in spatial sound reproduction systems through perceptual considerations

[ES] La reproducción de las propiedades espaciales del sonido es una cuestión cada vez más importante en muchas aplicaciones inmersivas emergentes. Ya sea en la reproducción de contenido audiovisual en entornos domésticos o en cines, en sistemas de videoconferencia inmersiva o en sistemas de realidad virtual o aumentada, el sonido espacial es crucial para una sensación de inmersión realista. La audición, más allá de la física del sonido, es un fenómeno perceptual influenciado por procesos cognitivos. El objetivo de esta tesis es contribuir con nuevos métodos y conocimiento a la optimización y simplificación de los sistemas de sonido espacial, desde un enfoque perceptual de la experiencia auditiva. Este trabajo trata en una primera parte algunos aspectos particulares relacionados con la reproducción espacial binaural del sonido, como son la escucha con auriculares y la personalización de la Función de Transferencia Relacionada con la Cabeza (Head Related Transfer Function - HRTF). Se ha realizado un estudio sobre la influencia de los auriculares en la percepción de la impresión espacial y la calidad, con especial atención a los efectos de la ecualización y la consiguiente distorsión no lineal. Con respecto a la individualización de la HRTF se presenta una implementación completa de un sistema de medida de HRTF y se introduce un nuevo método para la medida de HRTF en salas no anecoicas. Además, se han realizado dos experimentos diferentes y complementarios que han dado como resultado dos herramientas que pueden ser utilizadas en procesos de individualización de la HRTF, un modelo paramétrico del módulo de la HRTF y un ajuste por escalado de la Diferencia de Tiempo Interaural (Interaural Time Difference - ITD). En una segunda parte sobre reproducción con altavoces, se han evaluado distintas técnicas como la Síntesis de Campo de Ondas (Wave-Field Synthesis - WFS) o la panoramización por amplitud. Con experimentos perceptuales se han estudiado la capacidad de estos sistemas para producir sensación de distancia y la agudeza espacial con la que podemos percibir las fuentes sonoras si se dividen espectralmente y se reproducen en diferentes posiciones. Las aportaciones de esta investigación pretenden hacer más accesibles estas tecnologías al público en general, dada la demanda de experiencias y dispositivos audiovisuales que proporcionen mayor inmersión.[CA] La reproducció de les propietats espacials del so és una qüestió cada vegada més important en moltes aplicacions immersives emergents. Ja siga en la reproducció de contingut audiovisual en entorns domèstics o en cines, en sistemes de videoconferència immersius o en sistemes de realitat virtual o augmentada, el so espacial és crucial per a una sensació d'immersió realista. L'audició, més enllà de la física del so, és un fenomen perceptual influenciat per processos cognitius. L'objectiu d'aquesta tesi és contribuir a l'optimització i simplificació dels sistemes de so espacial amb nous mètodes i coneixement, des d'un criteri perceptual de l'experiència auditiva. Aquest treball tracta, en una primera part, alguns aspectes particulars relacionats amb la reproducció espacial binaural del so, com són l'audició amb auriculars i la personalització de la Funció de Transferència Relacionada amb el Cap (Head Related Transfer Function - HRTF). S'ha realitzat un estudi relacionat amb la influència dels auriculars en la percepció de la impressió espacial i la qualitat, dedicant especial atenció als efectes de l'equalització i la consegüent distorsió no lineal. Respecte a la individualització de la HRTF, es presenta una implementació completa d'un sistema de mesura de HRTF i s'inclou un nou mètode per a la mesura de HRTF en sales no anecoiques. A mès, s'han realitzat dos experiments diferents i complementaris que han donat com a resultat dues eines que poden ser utilitzades en processos d'individualització de la HRTF, un model paramètric del mòdul de la HRTF i un ajustament per escala de la Diferencià del Temps Interaural (Interaural Time Difference - ITD). En una segona part relacionada amb la reproducció amb altaveus, s'han avaluat distintes tècniques com la Síntesi de Camp d'Ones (Wave-Field Synthesis - WFS) o la panoramització per amplitud. Amb experiments perceptuals, s'ha estudiat la capacitat d'aquests sistemes per a produir una sensació de distància i l'agudesa espacial amb que podem percebre les fonts sonores, si es divideixen espectralment i es reprodueixen en diferents posicions. Les aportacions d'aquesta investigació volen fer més accessibles aquestes tecnologies al públic en general, degut a la demanda d'experiències i dispositius audiovisuals que proporcionen major immersió.[EN] The reproduction of the spatial properties of sound is an increasingly important concern in many emerging immersive applications. Whether it is the reproduction of audiovisual content in home environments or in cinemas, immersive video conferencing systems or virtual or augmented reality systems, spatial sound is crucial for a realistic sense of immersion. Hearing, beyond the physics of sound, is a perceptual phenomenon influenced by cognitive processes. The objective of this thesis is to contribute with new methods and knowledge to the optimization and simplification of spatial sound systems, from a perceptual approach to the hearing experience. This dissertation deals in a first part with some particular aspects related to the binaural spatial reproduction of sound, such as listening with headphones and the customization of the Head Related Transfer Function (HRTF). A study has been carried out on the influence of headphones on the perception of spatial impression and quality, with particular attention to the effects of equalization and subsequent non-linear distortion. With regard to the individualization of the HRTF a complete implementation of a HRTF measurement system is presented, and a new method for the measurement of HRTF in non-anechoic conditions is introduced. In addition, two different and complementary experiments have been carried out resulting in two tools that can be used in HRTF individualization processes, a parametric model of the HRTF magnitude and an Interaural Time Difference (ITD) scaling adjustment. In a second part concerning loudspeaker reproduction, different techniques such as Wave-Field Synthesis (WFS) or amplitude panning have been evaluated. With perceptual experiments it has been studied the capacity of these systems to produce a sensation of distance, and the spatial acuity with which we can perceive the sound sources if they are spectrally split and reproduced in different positions. The contributions of this research are intended to make these technologies more accessible to the general public, given the demand for audiovisual experiences and devices with increasing immersion.Gutiérrez Parera, P. (2020). Optimization and improvements in spatial sound reproduction systems through perceptual considerations [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/142696TESI

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

Strategy of Image Quality Assessment a New Fidelity Metric Based Upon Distortion Contrast Decoupling

This thesis presents a new image fidelity metric, Most Apparent Distortion (MAD), that uses a visual masking model and a measure of appearance distortion strategically to define the fidelity of a distorted image. Subjective image fidelity has been shown to be largely influenced by visual contrast masking of distortions and distortion energy. However, recent image fidelity metrics without an explicit visual masking model have been shown to correlate highly with subjective ratings. We argue that, at high quality, viewers use a different strategy for the task of rating distorted images than at low quality. We then evaluate the performance of MAD on two fidelity databases. In particular, we compare the performance of MAD to Peak Signal to Noise Ratio, Visual Signal to Noise Ratio, Structural Similarity, and Visual Information Fidelity. The results show that MAD performs statistically better than all other fidelity algorithms using various evaluation criteria for both databases.School of Electrical & Computer Engineerin

Subjective and objective quality evaluation of synthetic and high dynamic range images

Recent years have seen a huge growth in the acquisition, transmission, and storage of videos. The visual data consists of both natural scenes as well as synthetic scenes, such as animated movies, cartoons and video games. In all these cases, the ultimate goal is to provide the viewers with a satisfactory quality-of-experience. In addition to the traditional 8-bit images, high dynamic range imaging is also becoming popular because of its ability to represent the real world luminances more realistically. Coming up with objective image quality assessment algorithms for these applications is an interesting research problem. In this work, I have developed a synthetic image quality database by introducing varying degrees of different types of distortions and conducted a subjective experiment in order to obtain the ground-truth data. I evaluated the performance of state-of-the-art image quality assessment algorithms (typically meant for natural images) on this database, especially no-reference algorithms that have not been applied to the domain of computer graphics images before. I identified the top-performing algorithms along with analyzing the types of distortions on which the present algorithms show a less impressive performance. For high dynamic range(HDR) images, I have designed two new full-reference image quality assessment algorithms to judge the quality of tonemapped HDR images using statistical features extracted from them. I have also conducted a massive online crowd-sourced subjective test for HDR image artifacts arising from tonemapping, multiple-exposure fusion and post processing. To the best of our knowledge, presently this is the largest HDR image database in the world involving the largest number of source images and most number of human evaluations. Based on the subjective evaluations obtained, I have also proposed machine learning based no-reference image quality assessment algorithms to predict the perceptual quality of HDR images.Electrical and Computer Engineerin

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics. © 2011 Elsevier Ltd. All rights reserved