Efficient Integrated Coding for Compound Image

The development of computer and network technologies, image with mixed text, graphics and natural picture are seen everywhere, such as captured screen, web page, scanned electronic documents, slides, posters, compound images applications of computer screen image transmission. It is used to reduce the amount of data required to present the digital image and to improve the appearance of an image to a human observer an extract quantitative information. To compress a compound image various types of lossy and lossless algorithms are used. This research work deals with the preprocessing, macroblock divisions, transformations, quantization, text block and non used to compress a compound image to produce a high compression ratio, less compression time and decompression time and high PSNR value than the existing method. KeywordsPreprocessing, Transformations, Q Digital image processing refers to processing digital images by means of a digital computer. It is composed of finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, digital elements, pels and the pixels elements of a digital image. Digital image processing is used in many applications like industrial inspection for tracking and monitoring, medical images for visualization, reconstruction, aircraft correction and disease quantification, satellite imaging for weather and environmental conditions monitoring, telecommunication, cinema, image synthesis, scientific visualization and law enforcement f checking and speed [2] [3]. Example for the Compound image is shown in the Figure.1. Here preprocessing is used to improve the quality of images. If the images are too noisy or images should be filtered and sharpened. In image processing, filters are mainly used to suppress the high frequencies in the image [4] [5]. The various types of After preprocessing an image, the images should be segmented and compression has to be done. There are two types of compressions like lossy and lossless compression. For lossy and lossless compression various algorithms are used to compress a compound image. After compression, made to get the original compound image. The paper is structured as follows. In section 2 Proposed systems. In Section 4 Methodology is dealt with. deals with the Feature enhancement. Vol. 2: No. 2, July September