Logarithmical hopping encoding: a low computational complexity algorithm for image compression

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber–Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O( n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient.

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity.

Visual attention and perception models for assessing quality in 2D and 3D stereoscopic video

La medida de calidad de vídeo sigue siendo necesaria para definir los criterios que caracterizan una señal que cumpla los requisitos de visionado impuestos por el usuario. Las nuevas tecnologías, como el vídeo 3D estereoscópico o formatos más allá de la alta definición, imponen nuevos criterios que deben ser analizadas para obtener la mayor satisfacción posible del usuario. Entre los problemas detectados durante el desarrollo de esta tesis doctoral se han determinado fenómenos que afectan a distintas fases de la cadena de producción audiovisual y tipo de contenido variado. En primer lugar, el proceso de generación de contenidos debe encontrarse controlado mediante parámetros que eviten que se produzca el disconfort visual y, consecuentemente, fatiga visual, especialmente en lo relativo a contenidos de 3D estereoscópico, tanto de animación como de acción real. Por otro lado, la medida de calidad relativa a la fase de compresión de vídeo emplea métricas que en ocasiones no se encuentran adaptadas a la percepción del usuario. El empleo de modelos psicovisuales y diagramas de atención visual permitirían ponderar las áreas de la imagen de manera que se preste mayor importancia a los píxeles que el usuario enfocará con mayor probabilidad. Estos dos bloques se relacionan a través de la definición del término saliencia. Saliencia es la capacidad del sistema visual para caracterizar una imagen visualizada ponderando las áreas que más atractivas resultan al ojo humano. La saliencia en generación de contenidos estereoscópicos se refiere principalmente a la profundidad simulada mediante la ilusión óptica, medida en términos de distancia del objeto virtual al ojo humano. Sin embargo, en vídeo bidimensional, la saliencia no se basa en la profundidad, sino en otros elementos adicionales, como el movimiento, el nivel de detalle, la posición de los píxeles o la aparición de caras, que serán los factores básicos que compondrán el modelo de atención visual desarrollado. Con el objetivo de detectar las características de una secuencia de vídeo estereoscópico que, con mayor probabilidad, pueden generar disconfort visual, se consultó la extensa literatura relativa a este tema y se realizaron unas pruebas subjetivas preliminares con usuarios. De esta forma, se llegó a la conclusión de que se producía disconfort en los casos en que se producía un cambio abrupto en la distribución de profundidades simuladas de la imagen, aparte de otras degradaciones como la denominada “violación de ventana”. A través de nuevas pruebas subjetivas centradas en analizar estos efectos con diferentes distribuciones de profundidades, se trataron de concretar los parámetros que definían esta imagen. Los resultados de las pruebas demuestran que los cambios abruptos en imágenes se producen en entornos con movimientos y disparidades negativas elevadas que producen interferencias en los procesos de acomodación y vergencia del ojo humano, así como una necesidad en el aumento de los tiempos de enfoque del cristalino. En la mejora de las métricas de calidad a través de modelos que se adaptan al sistema visual humano, se realizaron también pruebas subjetivas que ayudaron a determinar la importancia de cada uno de los factores a la hora de enmascarar una determinada degradación. Los resultados demuestran una ligera mejora en los resultados obtenidos al aplicar máscaras de ponderación y atención visual, los cuales aproximan los parámetros de calidad objetiva a la respuesta del ojo humano. ABSTRACT Video quality assessment is still a necessary tool for defining the criteria to characterize a signal with the viewing requirements imposed by the final user. New technologies, such as 3D stereoscopic video and formats of HD and beyond HD oblige to develop new analysis of video features for obtaining the highest user’s satisfaction. Among the problems detected during the process of this doctoral thesis, it has been determined that some phenomena affect to different phases in the audiovisual production chain, apart from the type of content. On first instance, the generation of contents process should be enough controlled through parameters that avoid the occurrence of visual discomfort in observer’s eye, and consequently, visual fatigue. It is especially necessary controlling sequences of stereoscopic 3D, with both animation and live-action contents. On the other hand, video quality assessment, related to compression processes, should be improved because some objective metrics are adapted to user’s perception. The use of psychovisual models and visual attention diagrams allow the weighting of image regions of interest, giving more importance to the areas which the user will focus most probably. These two work fields are related together through the definition of the term saliency. Saliency is the capacity of human visual system for characterizing an image, highlighting the areas which result more attractive to the human eye. Saliency in generation of 3DTV contents refers mainly to the simulated depth of the optic illusion, i.e. the distance from the virtual object to the human eye. On the other hand, saliency is not based on virtual depth, but on other features, such as motion, level of detail, position of pixels in the frame or face detection, which are the basic features that are part of the developed visual attention model, as demonstrated with tests. Extensive literature involving visual comfort assessment was looked up, and the development of new preliminary subjective assessment with users was performed, in order to detect the features that increase the probability of discomfort to occur. With this methodology, the conclusions drawn confirmed that one common source of visual discomfort was when an abrupt change of disparity happened in video transitions, apart from other degradations, such as window violation. New quality assessment was performed to quantify the distribution of disparities over different sequences. The results confirmed that abrupt changes in negative parallax environment produce accommodation-vergence mismatches derived from the increasing time for human crystalline to focus the virtual objects. On the other side, for developing metrics that adapt to human visual system, additional subjective tests were developed to determine the importance of each factor, which masks a concrete distortion. Results demonstrated slight improvement after applying visual attention to objective metrics. This process of weighing pixels approximates the quality results to human eye’s response.

Image enhancement with MatLab algorithms

Nowadays, a lot of applications use digital images. For example in face recognition to detect and tag persons in photograph, for security control, and a lot of applications that can be found in smart cities, as speed control in roads or highways and cameras in traffic lights to detect drivers ignoring red light. Also in medicine digital images are used, such as x-ray, scanners, etc. These applications depend on the quality of the image obtained. A good camera is expensive, and the image obtained depends also on external factor as light. To make these applications work properly, image enhancement is as important as, for example, a good face detection algorithm. Image enhancement also can be used in normal photograph, for pictures done in bad light conditions, or just to improve the contrast of an image. There are some applications for smartphones that allow users apply filters or change the bright, colour or contrast on the pictures. This project compares four different techniques to use in image enhancement. After applying one of these techniques to an image, it will use better the whole available dynamic range. Some of the algorithms are designed for grey scale images and others for colour images. It is used Matlab software to develop and present the final results. These algorithms are Successive Means Quantization Transform (SMQT), Histogram Equalization, using Matlab function and own implemented function, and V transform. Finally, as conclusions, we can prove that Histogram equalization algorithm is the simplest of all, it has a wide variability of grey levels and it is not suitable for colour images. V transform algorithm is a good option for colour images. The algorithm is linear and requires low computational power. SMQT algorithm is non-linear, insensitive to gain and bias and it can extract structure of the data. RESUMEN. Hoy en día incontable número de aplicaciones usan imágenes digitales. Por ejemplo, para el control de la seguridad se usa el reconocimiento de rostros para detectar y etiquetar personas en fotografías o vídeos, para distintos usos de las ciudades inteligentes, como control de velocidad en carreteras o autopistas, cámaras en los semáforos para detectar a conductores haciendo caso omiso de un semáforo en rojo, etc. También en la medicina se utilizan imágenes digitales, como por ejemplo, rayos X, escáneres, etc. Todas estas aplicaciones dependen de la calidad de la imagen obtenida. Una buena cámara es cara, y la imagen obtenida depende también de factores externos como la luz. Para hacer que estas aplicaciones funciones correctamente, el tratamiento de imagen es tan importante como, por ejemplo, un buen algoritmo de detección de rostros. La mejora de la imagen también se puede utilizar en la fotografía no profesional o de consumo, para las fotos realizadas en malas condiciones de luz, o simplemente para mejorar el contraste de una imagen. Existen aplicaciones para teléfonos móviles que permiten a los usuarios aplicar filtros y cambiar el brillo, el color o el contraste en las imágenes. Este proyecto compara cuatro técnicas diferentes para utilizar el tratamiento de imagen. Se utiliza la herramienta de software matemático Matlab para desarrollar y presentar los resultados finales. Estos algoritmos son Successive Means Quantization Transform (SMQT), Ecualización del histograma, usando la propia función de Matlab y una nueva función que se desarrolla en este proyecto y, por último, una función de transformada V. Finalmente, como conclusión, podemos comprobar que el algoritmo de Ecualización del histograma es el más simple de todos, tiene una amplia variabilidad de niveles de gris y no es adecuado para imágenes en color. El algoritmo de transformada V es una buena opción para imágenes en color, es lineal y requiere baja potencia de cálculo. El algoritmo SMQT no es lineal, insensible a la ganancia y polarización y, gracias a él, se puede extraer la estructura de los datos.

Quality of experience of adaptive video streaming: investigation in service parameters and subjective quality assessment methodology

The usage of HTTP adaptive streaming (HAS) has become widely spread in multimedia services. Because it allows the service providers to improve the network resource utilization and user׳s Quality of Experience (QoE). Using this technology, the video playback interruption is reduced since the network and server status in addition to capability of user device, all are taken into account by HAS client to adapt the quality to the current condition. Adaptation can be done using different strategies. In order to provide optimal QoE, the perceptual impact of adaptation strategies from point of view of the user should be studied. However, the time-varying video quality due to the adaptation which usually takes place in a long interval introduces a new type of impairment making the subjective evaluation of adaptive streaming system challenging. The contribution of this paper is two-fold: first, it investigates the testing methodology to evaluate HAS QoE by comparing the subjective experimental outcomes obtained from ACR standardized method and a semi-continuous method developed to evaluate the long sequences. In addition, influence of using audiovisual stimuli to evaluate the video-related impairment is inquired. Second, impact of some of the adaptation technical factors including the quality switching amplitude and chunk size in combination with high range of commercial content type is investigated. The results of this study provide a good insight toward achieving appropriate testing method to evaluate HAS QoE, in addition to designing switching strategies with optimal visual quality.

Smart and sustainable public transport systems through improving level and quality of service

Buses are considered a slow, low comfort and low reliability transport system, thus its negative and por image. In the framework of the 3iBS project (2012), several examples of innovative and/or effective solutions regarding the Level of Service (LoS) were analysed aiming to provide operators, practitioners and policy makers with a set of Good Practice Guidelines to strengthen the competitiveness of the bus in the urban environment. The identification of the key indicators regarding vehicles, infrastructure and operation was possible through the analysis of a set of case studies -among which Barcelona (Spain), Cagliari (Italy), London (United Kingdom), Paris and Nantes (France). A cross comparison between the case studies was carried out for contrasting the level of achievement of the different criteria considered. The information provided on Regulatory, Financial and Technical issues allows the identification of a number of specific factors influencing the implementation of a high quality transport scheme, and set the basis for the elaboration of a set of Guidelines for the implementation of an intelligent, innovative and integrated bus system, including the main barriers to be tackled.