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)

Task-specific observer performance and image quality : direct and indirect relationships in low-dose CT images

Aim: The aim of this research was to examine image quality in low-dose CT images, as determined by a range of image quality measures (IQM), in addition to psychophysical assessment, and look at direct or indirect relationships. Method: CT images of an anthropomorphic chest phantom were obtained using increasing tube current (mA) to vary image quality. Tube current was increased incrementally (15mA- 100mA) and 200mA (reference image). Three sets of simulated lesions (sizes 5mm, 8mm and 10mm) of density 100HU, -630HU and -800HU were imaged one density at a time, using a 16 slice CT. A normal series was also acquired. These image sets were repeated using attenuation jackets, to achieve further image degradation. Images (5mm slice thickness) were reconstructed using filtered back-projection. Image analyses were carried out on 235 images of which 39 were normal. IQMs used were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), non-shift-edge ratio (NSER), texture analysis and 3D noise power spectrum (NPS). Visual grading characteristic (VGC) observer performance studies were performed with seven observers using a localisation task, and observer tasks involving visualisation of structures with how well images fulfilled international CT quality criteria, and the impact of noise on those decisions. Results and conclusion: The relationship of mA to IQM values was mainly logarithmic, with patterns of undulations in graphed data indicating potential for reduction in radiation dose. Additionally, attenuation jackets significantly affected IQM values, as did regional variation. NSER, Energy(uniformity) and Homogeneity showed the strongest inter-correlation and an inverse relationship with Entropy. Observer performance studies demonstrated no significant difference in lesion localisations at different amperage, however there was strong correlation between the impact of image noise and the fulfilment of visualisation criteria in CT images. NSER, Energy (uniformity), Homogeneity and Entropy had moderate to strong correlation with human observers. This research demonstrated the importance of simulating body habitus, and using the appropriate IQM, when assessing image quality for the task intended