The JPEG2000 still image compression standard

The development of standards (emerging and established) by the International Organization for Standardization (ISO), the International Telecommunications Union (ITU), and the International Electrotechnical Commission (IEC) for audio, image, and video, for both transmission and storage, has led to worldwide activity in developing hardware and software systems and products applicable to a number of diverse disciplines [7], [22], [23], [55], [56], [73]. Although the standards implicitly address the basic encoding operations, there is freedom and flexibility in the actual design and development of devices. This is because only the syntax and semantics of the bit stream for decoding are specified by standards, their main objective being the compatibility and interoperability among the systems (hardware/software) manufactured by different companies. There is, thus, much room for innovation and ingenuity. Since the mid 1980s, members from both the ITU and the ISO have been working together to establish a joint international standard for the compression of grayscale and color still images. This effort has been known as JPEG, the Join

Processing and codification images based on jpg standard

This project raises the necessity to use the image compression currently, and the different methods of compression and codification. Specifically, it will deepen the lossy compression standards with the JPEG [1] standard. The main goal of this project is to implement a Matlab program, which encode and compress an image of any format in a “jpg” format image, through JPEG standard premises. JPEG compresses images based on their spatial frequency, or level of detail in the image. Areas with low levels of detail, like blue sky, are compressed better than areas with high levels of detail, like hair, blades of trees, or hard-edged transitions. The JPEG algorithm takes advantage of the human eye's increased sensitivity to small differences in brightness versus small differences in color, especially at higher frequencies. The JPEG algorithm first transforms the image from RGB to the luminance/chrominance (Y-Cb-Cr) color space, or brightness/grayscale (Y) from the two color components. The algorithm then downsamples the color components and leaves the brightness component alone. Next, the JPEG algorithm approximates 8x8 blocks of pixels with a base value representing the average, plus some frequency coefficients for nearby variations. Quantization, then downsamples these DCT coefficients. Higher frequencies and chroma are quantized by larger coefficients than lower frequencies and luminance. Thus more of the brightness information is kept than the higher frequencies and color values. So the lower the level of detail and the fewer abrupt color or tonal transitions, the more efficient the JPEG algorithm becomes. ____________________________________________________________________________________________________________________________En este proyecto se aborda la necesidad de comprimir las imágenes en la actualidad, además de explicar los diferentes métodos posibles para la compresión y codificación de imágenes. En concreto, se va a profundizar en los estándares de compresión con pérdidas, mediante el estándar JPEG. El pilar central del proyecto será la realización de un programa en Matlab que codifique y comprima una imagen de cualquier formato en una imagen con formato “jpg”, mediante las premisas del estándar JPEG. La compresión de imágenes con JPEG está basada en la frecuencia espacial, o nivel de detalle, de las imágenes. Las áreas con bajo nivel de detalle, es decir, homogéneas, se pueden comprimir mejor que áreas con gran nivel de detalle o las transiciones de los bordes. El algoritmo JPEG se aprovecha de la sensibilidad del ojo humano a pequeñas diferencias de brillo frente a las de color, especialmente con altas frecuencias. El algoritmo JPEG primero transforma la paleta de colores de la imagen RGB a un espacio de color de luminancia/crominancia (Y-Cb-Cr), o brillo/escala de grises (Y) con las dos componentes del color. El algoritmo a continuación disminuye las componentes del color y deja solo la componente del brillo. A continuación, se aproxima la imagen en bloques de 8x8 pixeles con un valor base promedio, además de coeficientes de frecuencia de variaciones cercanas. Con la cuantificación, se disminuyen la resolución de los coeficientes de la DCT. Las frecuencias más altas y crominancias se cuantifican con los coeficientes de bajas frecuencias y luminancia. De esta forma, se mantienen mayor información de brillo que de altas frecuencias y colores. Por lo tanto, cuanto más homogénea sea la imagen (menor nivel de detalle y menos transiciones tonales abruptas) más eficiente será el algoritmo JPEG.Ingeniería Técnica en Sistemas de Telecomunicació

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy