The Impact of Spatial Masking in Image Quality Meters

Compression of digital image and video leads to block-based visible distortions like blockiness. The PSNR quality metric doesn2019;t correlate well with the subjective metric as it doesn2019;t take into consideration the impact of human visual system. In this work, we study the impact of human visual system in masking the coding distortions and its effect on the accuracy of the quality meter. We have chosen blockiness which is the most common coding distortion in DCTbased JPEG or intracoded video. We have studied the role of spatial masking by applying different masking techniques on full, reduced and no reference meters. As the visibility of distortion is content dependent, the distortion needs to be masked according to the spatial activity of the image. The results show that the complexity of spatial masking may be reduced by using the reference information efficiently. For full and reduced reference meters the spatial masking hasn2019;t much importance, if the blockiness detection is accurate, while for the no reference meter spatial masking is required to compensate the absence of any required reference information

Importance of Frame Selection in Video Quality Assessment

Video sequences contains multiple frames therefore their quality is estimated by determining individual quality metric of each frame then apply the temporal masking affect. However, the integration of each frame’s quality metric into one score is very important because each video frame has different spatial features hence have different quality metric. There are several methods available to combine the metric into one score like averaging, linear weighting, worst frames averaging etc. Taking the average of each frame’s score is not very useful as humans give more attention to the worst values (most distorted frame) while rating their values. In this paper we evaluated the performance of different integration methods and a different approach is proposed which includes the average of worst selected frames which is discussed in later sections. The work is tested on LIVE video database which consists of 40 video sequences. They have provided the mean opinion scores for each video with the database. The correlation coefficient of 88.21% is achieved when tested with the best model designed

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)