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Content-Based Image Processing Techniques Research in Compressed Domain

Author: LiuBo
Tutor: KuXiShu
School: National University of Defense Science and Technology
Course: Electronic Science and Technology
Keywords: Compressed Domain Discrete Cosine Transform Wavelet Transform Contour Extraction Character positioning
CLC: TP391.41
Type: Master's thesis
Year: 2007
Downloads: 76
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Abstract


The traditional image processing technology is pixel domain data on a large number of image data in compressed form, must be unpacked to its processing, which will affect the system's real-time and flexibility. Therefore, based on the compressed domain image processing methods to become one of the research focus in recent years. The discrete cosine transform DCT is earliest for image compression technology, existing compressed domain image processing techniques are mostly concentrated in the DCT compressed domain; wavelet transform time-frequency localized features and multi-resolution structure it in image coding a wide range of applications, so study the discrete wavelet transform domain of image processing technology will be of great significance. In this paper, the current international video coding standard DCT domain and wavelet domain, how fast and efficient image processing. First, this paper systematically expounded the basic idea of ??compressed domain processing and its research methods, analysis and image characteristics specific manifestations. JPEG and JPEG2000 is mainly of two types of static image coding standard, the paper focuses on the use of two types of standard compression algorithm, and analyzed the characteristics of two types of standard coding combined with practical application. Then, the article DCT compressed domain image processing technology. Through the analysis of the characteristics of the discrete cosine transform and JPEG encoding, the study of the characteristics of the data distribution of the image by DCT processing, and the algorithm is proposed a use of the DCT block coefficients for quick extraction of the image contours in accordance with its characteristics, this method first extracts the DC coefficient in each 8 × 8 DCT blocks of data and two adjacent AC coefficients, subjecting them to the appropriate organization coarse positioning of the image contour corresponding threshold value is set, according to the statistical distribution of the coefficients of law, in the image The post-treatment process, the use of curve fitting techniques interrupter image boundary is closed, to achieve the extraction of the image contours in the case of not completely extracting. Finally, we focus on wavelet compression domain image processing technology. Wavelet transform technology, in-depth research and summarizes the characteristics of the image after wavelet transform coefficient, and put forward a mathematical morphology wavelet high frequency coefficients directly processed to extract the fast algorithm of the target object in the image, it significantly to the low scale of the wavelet transform domain coefficient for handling objects, process data less simple computation, simulation results show that the algorithm can quickly extract the target object has a certain practicality; article first wavelet domain processing The two aspects of the positioning of the characters in the image. Positioning technology combined with a wavelet domain character positioning algorithm, the algorithm uses an 'm' shaped structure on the weighted wavelet domain data processing, combined with the region of pixel-based clustering algorithm realized character area and non- character area classification, Finally, the projection histogram to locate the character area, the simulation results show that this method can effectively and quickly detected the characters in the image area has better robustness, multi-scale character positioning and noise immunity.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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