AN EFFICIENT NO-REFERENCE METRIC FOR PERCEIVED BLUR

International audienceThis paper presents an efficient no-reference metric that quantifies perceived image quality induced by blur. Instead of explicitly simulating the human visual perception of blur, it calculates the local edge blur in a cost-effective way, and applies an adaptive neural network to empirically learn the highly nonlinear relationship between the local values and the overall image quality. Evaluation of the proposed metric using the LIVE blur database shows its high prediction accuracy at a largely reduced computational cost. To further validate the performance of the blur metric on its robustness against different image content, two additional quality perception experiments were conducted: one with highly textured natural images and one with images with an intentionally blurred background . Experimental results demonstrate that the proposed blur metric is promising for real-world applications both in terms of computational efficiency and practical reliability

A bag of words description scheme for image quality assessment

Every day millions of images are obtained, processed, compressed, saved, transmitted and reproduced. All these operations can cause distortions that affect their quality. The quality of these images should be measured subjectively. However, that brings the disadvantage of achieving a considerable number of tests with individuals requested to provide a statistical analysis of an image’s perceptual quality. Several objective metrics have been developed, that try to model the human perception of quality. However, in most applications the representation of human quality perception given by these metrics is far from the desired representation. Therefore, this work proposes the usage of machine learning models that allow for a better approximation. In this work, definitions for image and quality are given and some of the difficulties of the study of image quality are mentioned. Moreover, three metrics are initially explained. One uses the image’s original quality has a reference (SSIM) while the other two are no reference (BRISQUE and QAC). A comparison is made, showing a large discrepancy of values between the two kinds of metrics. The database that is used for the tests is TID2013. This database was chosen due to its dimension and by the fact of considering a large number of distortions. A study of each type of distortion in this database is made. Furthermore, some concepts of machine learning are introduced along with algorithms relevant in the context of this dissertation, notably, K-means, KNN and SVM. Description aggregator algorithms like “bag of words” and “fisher-vectors” are also mentioned. This dissertation studies a new model that combines machine learning and a quality metric for quality estimation. This model is based on the division of images in cells, where a specific metric is computed. With this division, it is possible to obtain local quality descriptors that will be aggregated using “bag of words”. A SVM with an RBF kernel is trained and tested on the same database and the results of the model are evaluated using cross-validation. The results are analysed using Pearson, Spearman and Kendall correlations and the RMSE to evaluate the representation of the model when compared with the subjective results. The model improves the results of the metric that was used and shows a new path to apply machine learning for quality evaluation.No nosso dia-a-dia as imagens são obtidas, processadas, comprimidas, guardadas, transmitidas e reproduzidas. Em qualquer destas operações podem ocorrer distorções que prejudicam a sua qualidade. A qualidade destas imagens pode ser medida de forma subjectiva, o que tem a desvantagem de serem necessários vários testes, a um número considerável de indivíduos para ser feita uma análise estatística da qualidade perceptual de uma imagem. Foram desenvolvidas várias métricas objectivas, que de alguma forma tentam modelar a percepção humana de qualidade. Todavia, em muitas aplicações a representação de percepção de qualidade humana dada por estas métricas fica aquém do desejável, razão porque se propõe neste trabalho usar modelos de reconhecimento de padrões que permitam uma maior aproximação. Neste trabalho, são dadas definições para imagem e qualidade e algumas das dificuldades do estudo da qualidade de imagem são referidas. É referida a importância da qualidade de imagem como ramo de estudo, e são estudadas diversas métricas de qualidade. São explicadas três métricas, uma delas que usa a qualidade original como referência (SSIM) e duas métricas sem referência (BRISQUE e QAC). Uma comparação é feita entre elas, mostrando- – se uma grande discrepância de valores entre os dois tipos de métricas. Para os testes feitos é usada a base de dados TID2013, que é muitas vezes considerada para estudos de qualidade de métricas devido à sua dimensão e ao facto de considerar um grande número de distorções. Neste trabalho também se fez um estudo dos tipos de distorção incluidos nesta base de dados e como é que eles são simulados. São introduzidos também alguns conceitos teóricos de reconhecimento de padrões e alguns algoritmos relevantes no contexto da dissertação, são descritos como o K-means, KNN e as SVMs. Algoritmos de agregação de descritores como o “bag of words” e o “fisher-vectors” também são referidos. Esta dissertação adiciona métodos de reconhecimento de padrões a métricas objectivas de qua– lidade de imagem. Uma nova técnica é proposta, baseada na divisão de imagens em células, nas quais uma métrica será calculada. Esta divisão permite obter descritores locais de qualidade que serão agregados usando “bag of words”. Uma SVM com kernel RBF é treinada e testada na mesma base de dados e os resultados do modelo são mostrados usando cross-validation. Os resultados são analisados usando as correlações de Pearson, Spearman e Kendall e o RMSE que permitem avaliar a proximidade entre a métrica desenvolvida e os resultados subjectivos. Este modelo melhora os resultados obtidos com a métrica usada e demonstra uma nova forma de aplicar modelos de reconhecimento de padrões ao estudo de avaliação de qualidade

Quality assessment metric of stereo images considering cyclopean integration and visual saliency

In recent years, there has been great progress in the wider use of three-dimensional (3D) technologies. With increasing sources of 3D content, a useful tool is needed to evaluate the perceived quality of the 3D videos/images. This paper puts forward a framework to evaluate the quality of stereoscopic images contaminated by possible symmetric or asymmetric distortions. Human visual system (HVS) studies reveal that binocular combination models and visual saliency are the two key factors for the stereoscopic image quality assessment (SIQA) metric. Therefore inspired by such findings in HVS, this paper proposes a novel saliency map in SIQA metric for the cyclopean image called “cyclopean saliency”, which avoids complex calculations and produces good results in detecting saliency regions. Moreover, experimental results show that our metric significantly outperforms conventional 2D quality metrics and yields higher correlations with human subjective judgment than the state-of-art SIQA metrics. 3D saliency performance is also compared with “cyclopean saliency” in SIQA. It is noticed that the proposed metric is applicable to both symmetric and asymmetric distortions. It can thus be concluded that the proposed SIQA metric can provide an effective evaluation tool to assess stereoscopic image quality

Optimizing Perceptual Quality Prediction Models for Multimedia Processing Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

3D Medical Image Lossless Compressor Using Deep Learning Approaches

The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in efficient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies effectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an effective compression method. Since medical information is critical and imposes an influential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable efforts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information efficiently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the differences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the effectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by different hospitals, representing different body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are significantly more informative than others, specifically in medical domains where samples are available on a scale of billions. The effectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling

Métodos sem referência baseados em características espaço-temporais para avaliação objetiva de qualidade de vídeo digital

The development of no-reference video quality assessment methods is an incipient topic in the literature and it is challenging in the sense that the results obtained by the proposed method should provide the best possible correlation with the evaluations of the Human Visual System. This thesis presents three proposals for objective no-reference video quality evaluation based on spatio-temporal features. The first approach uses a sigmoidal analytical model with leastsquares solution using the Levenberg-Marquardt method. The second and third approaches use a Single-Hidden Layer Feedforward Neural Network with learning based on the Extreme Learning Machine algorithm. Furthermore, an extended version of Extreme Learning Machine algorithm was developed which looks for the best parameters of the artificial neural network iteratively, according to a simple termination criteria, whose goal is to increase the correlation between the objective and subjective scores. The experimental results using cross-validation techniques indicate that the proposed methods are correlated to the Human Visual System scores. Therefore, they are suitable for the monitoring of video quality in broadcasting systems and over IP networks, and can be implemented in devices such as set-top boxes, ultrabooks, tablets, smartphones and Wireless Display (WiDi) devices.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)O desenvolvimento de métodos sem referência para avaliação de qualidade de vídeo é um assunto incipiente na literatura e desafiador, no sentido de que os resultados obtidos pelo método proposto devem apresentar a melhor correlação possível com a percepção do Sistema Visual Humano. Esta tese apresenta três propostas para avaliação objetiva de qualidade de vídeo sem referência baseadas em características espaço-temporais. A primeira abordagem segue um modelo analítico sigmoidal com solução de mínimos quadrados que usa o método Levenberg-Marquardt e a segunda e terceira abordagens utilizam uma rede neural artificial Single-Hidden Layer Feedforward Neural Network com aprendizado baseado no algoritmo Extreme Learning Machine. Além disso, foi desenvolvida uma versão estendida desse algoritmo que busca os melhores parâmetros da rede neural artificial de forma iterativa, segundo um simples critério de parada, cujo objetivo é aumentar a correlação entre os escores objetivos e subjetivos. Os resultados experimentais, que usam técnicas de validação cruzada, indicam que os escores dos métodos propostos apresentam alta correlação com as escores do Sistema Visual Humano. Logo, eles são adequados para o monitoramento de qualidade de vídeo em sistemas de radiodifusão e em redes IP, bem como podem ser implementados em dispositivos como decodificadores, ultrabooks, tablets, smartphones e em equipamentos Wireless Display (WiDi)