Test Segmentation of MRC Document Compression and Decompression by Using MATLAB

Abstract-The mixed raster content (MRC) standard specifies a framework for document compression which can dramatically improve the compression/ quality tradeoff as compared to traditional lossy image compression algorithms. The key to MRC compression is the separation of the document into foreground and background layers, represented as a binary mask. Therefore, the resulting quality and compression ratio of a MRC document encoder is highly dependent upon the segmentation algorithm used to compute the binary mask. The incorporated multi scale framework is used in order to improve the segmentation accuracy of text with varying size. In this paper, we propose a novel multi scale segmentation scheme for MRC document encoding based on the sequential application of two algorithms. The first algorithm, cost optimized segmentation (COS), is a block wise segmentation algorithm formulated in a global cost optimization framework. The second algorithm, connected component classification (CCC), refines the initial segmentation by classifying feature vectors of connected components using a Markov random field (MRF) model. The combined COS/CCC segmentation algorithms are then incorporated into a multi scale framework in order to improve the segmentation accuracy of text with varying size

Sub-pixel gradient 를 활용한 compound 영상 압축

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 김수환.컴퓨터 성능과 네트워크 속도가 발전함에 따라 컴퓨터 화면에 표시되는 compound image 의 기술은 다양한 전송 환경에서 비디오 및 양방향 서비스가 가능해졌다. 그러나 compound image는 다양한 종류의 영상이 복합적으로 나타나기 때문에 영상의 종류를 명확히 구분하고 각 종류에 맞는 영상 데이터 처리 방식이 필요하게 된다. 영상의 데이터 처리 방식이 복잡해 질수록 서버와 클라이언트의 성능 불균형은 데이터를 원활히 생성/재현 하지 못하는 문제를 가질 수 있다. Compound image 의 분류는 텍스트로 구성된 부분에 대하여 다른 종류의 영상으로 분류하지 않아야 한다. 이는 블록 단위로 구분하여 분류하는 방법에서 인접한 블록간에 서로 다른 코딩 방법을 적용하게 되면 사람이 느끼는 영상의 화질은 낮아지게 된다. 본 연구에서는 이러한 문제점을 해결하기 위해서 텍스트의 생성과정을 역이용한 sub-pixel gradient 블록 분류 방법을 제시한다. 평판 디스플레이에서는 텍스트의 부드러움을 표현하기 위하여 sub-pixel 단위로 컬러의 변화량을 조절하게 된다. 이를 whole-pixel의 단위로 영상을 구분하게 되면, 텍스트의 영역을 명확하게 구분하지 못한다. 본 연구에는 sub-pixel gradient 블록 분류 방법을 통하여 텍스트로 구성된 영역과 텍스트가 아닌 영역에 대한 판단이 정확히 이루어짐을 실험을 통하여 확인하였다. 텍스트의 코딩방법 중 손실 압축방법은 텍스트로 구성된 영상이 높은 주파수를 가지는 영상이기 때문에 양자화나 변환과정을 거치게 되면 영상의 손실이 커지게 된다. 하지만 무 손실 압축 방법은 높은 데이터 량을 가지게 되고, 영상 전송 속도가 높아져야 하는 문제가 발생한다. 본 연구에서는 sub-pixel gradient 방법을 이용한 텍스트 영역에 대한 코딩 방법을 제시한다. 텍스트 영상이 가지는 특성을 이용하여 영상에서 발생하는 기울기에 대하여 코딩을 진행한다. 이를 통하여 영상의 손실을 줄이고 텍스트의 가독성을 높일 수 있다. 동일한 압축률에서 다른 압축 알고리즘에 비하여 텍스트의 화질과 가독성이 뛰어남을 확인하였다. Compound image는 자연 영상과는 다르게 움직임이 단순하고 노이즈가 없다는 특성을 가진다. 이는 기존의 움직임 추정방법에 비하여 복잡도가 낮은 방법을 가능하게 한다. 본 연구에서는 이러한 compound image의 영상 특성을 이용한 그룹 움직임 추정 방법을 제시한다. 픽셀의 움직임을 확인하기 전에 영상의 분류에 따라 분류된 영역의 움직임을 먼저 파악하고 이를 통하여 최종적인 움직임을 추정하게 된다. 그룹 움직임 추정 방법을 사용하면 기존의 탐색영역 방법과 비교하여 탐색 영역을 최소화 할 수 있으며, 복잡도를 낮출 수 있음을 실험을 통하여 확인하였다.초 록 i 차 례 iii 그림 목차 vi 표 목 차 ix 제1장 서 론 1 1.1 연구 배경 1 1.2 연구 내용 4 1.3 논문 구성 6 제2장 텍스트 생성과정 및 기존압축방법 7 2.1 텍스트 생성과정 7 2.2 표준 영상 압축 방법 14 2.3 H.264 inter prediction 16 2.4 Compound image 의 압축 알고리즘 19 제3장 Sub-pixel gradient 블록 분류 방법 23 3.1 Background & Text color extraction 28 3.2 Text De-colorization 32 3.3 블록 분류 실험 결과 38 제4장 Sub-pixel Gradient text 블록 코딩 방법 46 4.1 Gradient fitting process 51 4.2 Text Coding 56 4.2.1 Gradient로 구성된 부분의 코딩방법 56 4.2.2 Gradient가 없는 부분의 코딩방법 57 4.2.3 local min/max 값 예측 57 4.2.4 Whole-pixel 코딩 59 4.2.5 화질 enhancement 60 4.3 텍스트 코딩 동작 64 4.3.1 텍스트 코딩 입력 65 4.3.2 Whole-pixel 코딩 1 66 4.3.3 역방향 Sub-pixel gradient 코딩 1 67 4.3.4 Local minimum 코딩 1 69 4.3.5 순방향 gradient 코딩 1 70 4.3.6 Local maximum 코딩 1 71 4.3.7 역방향 gradient 코딩 2 72 4.3.8 Local minimum 코딩 2 73 4.3.9 순방향 gradient 코딩 2 74 4.3.10 Whole-pixel 코딩 2 75 4.4 텍스트 블록 코딩 실험 결과 77 제5장 그룹 움직임 추정 방법 88 5.1 Block Grouping 94 5.2 Group Matching 97 5.3 Group motion vector calculation 101 5.4 그룹 움직임 추정 방법 실험 결과 104 제6장 결 론 109 참 고 문 헌 112 Abstract 119Docto

Scanline calculation of radial influence for image processing

Efficient methods for the calculation of radial influence are described and applied to two image processing problems, digital halftoning and mixed content image compression. The methods operate recursively on scanlines of image values, spreading intensity from scanline to scanline in proportions approximating a Cauchy distribution. For error diffusion halftoning, experiments show that this recursive scanline spreading provides an ideal pattern of distribution of error. Error diffusion using masks generated to provide this distribution of error alleviate error diffusion "worm" artifacts. The recursive scanline by scanline application of a spreading filter and a complementary filter can be used to reconstruct an image from its horizontal and vertical pixel difference values. When combined with the use of a downsampled image the reconstruction is robust to incomplete and quantized pixel difference data. Such gradient field integration methods are described in detail proceeding from representation of images by gradient values along contours through to a variety of efficient algorithms. Comparisons show that this form of gradient field integration by convolution provides reduced distortion compared to other high speed gradient integration methods. The reduced distortion can be attributed to success in approximating a radial pattern of influence. An approach to edge-based image compression is proposed using integration of gradient data along edge contours and regularly sampled low resolution image data. This edge-based image compression model is similar to previous sketch based image coding methods but allows a simple and efficient calculation of an edge-based approximation image. A low complexity implementation of this approach to compression is described. The implementation extracts and represents gradient data along edge contours as pixel differences and calculates an approximate image by performing integration of pixel difference data by scanline convolution. The implementation was developed as a prototype for compression of mixed content image data in printing systems. Compression results are reported and strengths and weaknesses of the implementation are identified

Optimum Implementation of Compound Compression of a Computer Screen for Real-Time Transmission in Low Network Bandwidth Environments

Remote working is becoming increasingly more prevalent in recent times. A large part of remote working involves sharing computer screens between servers and clients. The image content that is presented when sharing computer screens consists of both natural camera captured image data as well as computer generated graphics and text. The attributes of natural camera captured image data differ greatly to the attributes of computer generated image data. An image containing a mixture of both natural camera captured image and computer generated image data is known as a compound image. The research presented in this thesis focuses on the challenge of constructing a compound compression strategy to apply the ‘best fit’ compression algorithm for the mixed content found in a compound image. The research also involves analysis and classification of the types of data a given compound image may contain. While researching optimal types of compression, consideration is given to the computational overhead of a given algorithm because the research is being developed for real time systems such as cloud computing services, where latency has a detrimental impact on end user experience. The previous and current state of the art videos codec’s have been researched along many of the most current publishing’s from academia, to design and implement a novel approach to a low complexity compound compression algorithm that will be suitable for real time transmission. The compound compression algorithm will utilise a mixture of lossless and lossy compression algorithms with parameters that can be used to control the performance of the algorithm. An objective image quality assessment is needed to determine whether the proposed algorithm can produce an acceptable quality image after processing. Both traditional metrics such as Peak Signal to Noise Ratio will be used along with a new more modern approach specifically designed for compound images which is known as Structural Similarity Index will be used to define the quality of the decompressed Image. In finishing, the compression strategy will be tested on a set of generated compound images. Using open source software, the same images will be compressed with the previous and current state of the art video codec’s to compare the three main metrics, compression ratio, computational complexity and objective image quality

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

The 1992 4th NASA SERC Symposium on VLSI Design

Papers from the fourth annual NASA Symposium on VLSI Design, co-sponsored by the IEEE, are presented. Each year this symposium is organized by the NASA Space Engineering Research Center (SERC) at the University of Idaho and is held in conjunction with a quarterly meeting of the NASA Data System Technology Working Group (DSTWG). One task of the DSTWG is to develop new electronic technologies that will meet next generation electronic data system needs. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The NASA SERC is proud to offer, at its fourth symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories, the electronics industry, and universities. These speakers share insights into next generation advances that will serve as a basis for future VLSI design

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Doctor of Philosophy

dissertationOver 40 years ago, the first computer simulation of a protein was reported: the atomic motions of a 58 amino acid protein were simulated for few picoseconds. With today's supercomputers, simulations of large biomolecular systems with hundreds of thousands of atoms can reach biologically significant timescales. Through dynamics information biomolecular simulations can provide new insights into molecular structure and function to support the development of new drugs or therapies. While the recent advances in high-performance computing hardware and computational methods have enabled scientists to run longer simulations, they also created new challenges for data management. Investigators need to use local and national resources to run these simulations and store their output, which can reach terabytes of data on disk. Because of the wide variety of computational methods and software packages available to the community, no standard data representation has been established to describe the computational protocol and the output of these simulations, preventing data sharing and collaboration. Data exchange is also limited due to the lack of repositories and tools to summarize, index, and search biomolecular simulation datasets. In this dissertation a common data model for biomolecular simulations is proposed to guide the design of future databases and APIs. The data model was then extended to a controlled vocabulary that can be used in the context of the semantic web. Two different approaches to data management are also proposed. The iBIOMES repository offers a distributed environment where input and output files are indexed via common data elements. The repository includes a dynamic web interface to summarize, visualize, search, and download published data. A simpler tool, iBIOMES Lite, was developed to generate summaries of datasets hosted at remote sites where user privileges and/or IT resources might be limited. These two informatics-based approaches to data management offer new means for the community to keep track of distributed and heterogeneous biomolecular simulation data and create collaborative networks