Progressive transmission of pseudo-color images. Appendix 1: Item 4

The transmission of digital images can require considerable channel bandwidth. The cost of obtaining such a channel can be prohibitive, or the channel might simply not be available. In this case, progressive transmission (PT) can be useful. PT presents the user with a coarse initial image approximation, and then proceeds to refine it. In this way, the user tends to receive information about the content of the image sooner than if a sequential transmission method is used. PT finds application in image data base browsing, teleconferencing, medical and other applications. A PT scheme is developed for use with a particular type of image data, the pseudo-color or color mapped image. Such images consist of a table of colors called a colormap, plus a 2-D array of index values which indicate which colormap entry is to be used to display a given pixel. This type of image presents some unique problems for a PT coder, and techniques for overcoming these problems are developed. A computer simulation of the color mapped PT scheme is developed to evaluate its performance. Results of simulation using several test images are presented

Aspects of multi-resolutional foveal images for robot vision

Imperial Users onl

Multi-resolution graph-based representation for analysis of large histo-pathological images

La exploración de muestras histopatológicas es potencialmente una importante fuente de información para el diagnóstico de enfermedades, educación y entrenamiento médico. Convencionalmente, este tipo de muestras se observan a través de un microscopio óptico, sin embargo, recientes avances tecnológicos en el área de imagino logia han favorecido el desarrollo de la microscopía virtual, un conjunto de herramientas que permiten la navegación de versiones digitales de placas histopatológicas, también llamadas Placas Virtuales (PV). Ya que las PV son un conjunto de imágenes de campos visuales microscópicos, estas son imágenes de alta resolución. El gran tamaño de las PV representa un cuello de botella para cualquier estrategia de navegación, introduciendo considerables retardos en la presentación de la información al usuario y produciendo en consecuencia, navegaciones poco fluidas. Por esto, el desarrollo de métodos para acelerar la interacción en este tipo de aplicaciones impulsara su uso en áreas como la tele patología. Una de las aproximaciones más aceptadas respecto a este problema son las estrategias de predicción, en las cuales solo las regiones de interés son enviadas al usuario. Por tanto el reto en este tipo de aproximaciones es identificar las regiones relevantes para el patólogo, es decir aquellas que contienen significado semántico para el diagnostico. Los modelos de atención visual intentan emular la percepción visual humana, permitiendo identificar las pocas regiones de una escena que contienen la mayor cantidad de información. Sin embargo, la complejidad de las imágenes histopatológicas hace que este tipo de modelos sea insuficiente para identificar estructuras relevantes dentro de la muestra y se hace necesario utilizar nuevas fuentes de información. En este trabajo se presenta una novedosa aproximación estadística que permite construir un mapa de información relevante de la PV con el fin de permitir navegaciones fluidas en un microscopio virtual. Este trabajo utiliza como una primera aproximación a las regiones relevantes un mapa de saliencia obtenido a partir de un modelo de atención visual \bottom-up", el cual es modificado por conocimiento \top-down. Obtenido a partir de navegaciones de patólogos expertos sobre PVs. Las regiones de la PV son estructuradas en un grafo tipo árbol, de acuerdo a su nivel de relevancia, en esta estructura los nodos representan regiones espaciales de la PV y los arcos representan relaciones de inclusión entre ellas. El método presentado alcanza en promedio, una capacidad de predicción del 67 % de regiones de interés que fueron visitadas en una nueva navegación, asumiendo un caché de solo el 20 % del tamaño total de la placa virtual. Adicionalmente, con respecto a un método más simple, i. e. sin la representación del grafo, el método propuesto mejora en un 3 % la capacidad de identificación de regiones relevantes, lo cual es significativo en este contexto, ya que un porcentaje como este puede representar regiones de mega pixeles en una PV.Exploration of complete histological samples is a very important source of information for diagnosis, learning and medical training. These samples have been conventionally observed through an optical microscope, but recent advances in imaging technology have enabled the development of virtual microscopy, a set of tools that allows navigation of digitized versions of tissue samples (virtual slides or VS). Since VS are the set of several microscopic visual �elds they are high resolution images with huge storage spaces. The size of these images repre- sents a bottle-neck for any navigation strategy, introducing considerable delays in displaying information to the user, which results in non- uid navigations. Development of methodo- logies for accelerate interaction with such volume of data, represents a reinforcement of its use in real applications as telepathology. One of the most accepted methodologies regarding this problem are based on prediction policies, in which only the regions of current interest are provided to the user. Then, the challenge in these approaches is to identify relevant regions, which for VS exploration enclose semantic meaning for diagnosis. Classical visual attention models emulates the human visual perception allowing to identify relevant regions with highest quantity of information. However, complexity of histo-pathological images make insu�cient these models to identify all relevant structures in the sample, furthermore new information sources that lies the identi�cation of salient features is required. In this work is presented a novel statistical hybrid approach which enable to build up relevant informa- tion VS maps to achieve uent virtual navigation. This approach, uses as �rst relevance approximation, a saliency map obtained from a bottom-up visual attention model, which is modi�ed by a top-down relevance information obtained from actual VS explorations of expert pathologists. Image regions are organized in a graph structure according it's levels of relevance, in this structure nodes are related to spatial regions and edges represent belonging relationships between them. The method herein introduced achieve in average, a prediction capacity of 67 % of RoIs that were visited in a new navigation, assuming a cache of only 20 % of total size of VS. Additionally, with respect to a simpler method, i.e. without graph representation, graph-based method outperforms RoIs identi�cation capacity in about 3 % which is signi�cant in this context since such a percentage could represent regions of mega- pixels of a VS.Maestrí

LOCMIC:LOw Complexity Multi-resolution Image Compression

Image compression is a well-established and extensively researched field. The huge interest in it has been aroused by the rapid enhancements introduced in imaging techniques and the various applications that use high-resolution images (e.g. medical, astronomical, Internet applications). The image compression algorithms should not only give state-of-art performance, they should also provide other features and functionalities such as progressive transmission. Often, a rough approximation (thumbnail) of an image is sufficient for the user to decide whether to continue the image transmission or to abort; which accordingly helps to reduce time and bandwidth. That in turn necessitated the development of multi-resolution image compression schemes. The existed multi-resolution schemes (e.g., Multi-Level Progressive method) have shown high computational efficiency, but with a lack of the compression performance, in general. In this thesis, a LOw Complexity Multi-resolution Image Compression (LOCMIC) based on the Hierarchical INTerpolation (HINT) framework is presented. Moreover, a novel integration of the Just Noticeable Distortion (JND) for perceptual coding with the HINT framework to achieve a visual-lossless multi-resolution scheme has been proposed. In addition, various prediction formulas, a context-based prediction correction model and a multi-level Golomb parameter adaption approach have been investigated. The proposed LOCMIC (the lossless and the visual lossless) has contributed to the compression performance. The lossless LOCMIC has achieved a 3% reduced bit rate over LOCO-I, about 1% over JPEG2000, 3% over SPIHT, and 2% over CALIC. The Perceptual LOCMIC has been better in terms of bit rate than near-lossless JPEG-LS (at NEAR=2) with about 4.7%. Moreover, the decorrelation efficiency of the LOCMIC in terms of entropy has shown an advance of 2.8%, 4.5% than the MED and the conventional HINT respectively

Maximum Energy Subsampling: A General Scheme For Multi-resolution Image Representation And Analysis

Image descriptors play an important role in image representation and analysis. Multi-resolution image descriptors can effectively characterize complex images and extract their hidden information. Wavelets descriptors have been widely used in multi-resolution image analysis. However, making the wavelets transform shift and rotation invariant produces redundancy and requires complex matching processes. As to other multi-resolution descriptors, they usually depend on other theories or information, such as filtering function, prior-domain knowledge, etc.; that not only increases the computation complexity, but also generates errors. We propose a novel multi-resolution scheme that is capable of transforming any kind of image descriptor into its multi-resolution structure with high computation accuracy and efficiency. Our multi-resolution scheme is based on sub-sampling an image into an odd-even image tree. Through applying image descriptors to the odd-even image tree, we get the relative multi-resolution image descriptors. Multi-resolution analysis is based on downsampling expansion with maximum energy extraction followed by upsampling reconstruction. Since the maximum energy usually retained in the lowest frequency coefficients; we do maximum energy extraction through keeping the lowest coefficients from each resolution level. Our multi-resolution scheme can analyze images recursively and effectively without introducing artifacts or changes to the original images, produce multi-resolution representations, obtain higher resolution images only using information from lower resolutions, compress data, filter noise, extract effective image features and be implemented in parallel processing

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Progressive transmission of digital recurrent video.

by Wai-Wa Wilson Chan.Thesis (M.Sc.)--Chinese University of Hong Kong, 1992.Includes bibliographical references (leaves 79-80).Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Problem under study and scope --- p.4Chapter 1.2 --- Review of relevant research --- p.6Chapter 1.3 --- Objectives --- p.11Chapter 2. --- Theory --- p.12Chapter 2.1 --- Multi-resolution representation of digital video --- p.13Chapter 2.2 --- Performance measure of progressive algorithm --- p.15Chapter 2.3 --- Introduction to depth pyramid --- p.35Chapter 2.4 --- Introduction to spatial pyramid --- p.37Chapter 2.5 --- Introduction to temporal pyramid --- p.42Chapter 2.6 --- Proposed algorithm for progressive transmission using depth-spatial-temporal pyramid --- p.46Chapter 3. --- Experiment --- p.55Chapter 3.1 --- Simulation on depth pyramid --- p.59Chapter 3.2 --- Simulation on spatial pyramid --- p.60Chapter 3.3 --- Simulation on temporal pyramid --- p.62Chapter 3.4 --- Simulation on algorithm for progressive transmission using depth-spatial-temporal pyramid --- p.64Chapter 4. --- Conclusions and discussions --- p.74Chapter 5. --- Reference and Appendix --- p.7