Integrated system for image storage, retrieval, and transmission using wavelet transform
| Autor: | Dan Yu, Yawen Liu, Ray Yan Mu, Shi-Qiang Yang |
|---|---|
| Rok vydání: | 1998 |
| Předmět: |
Computer science
business.industry Stationary wavelet transform Second-generation wavelet transform ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Wavelet transform Data_CODINGANDINFORMATIONTHEORY Content-based image retrieval Wavelet packet decomposition Wavelet Image texture Computer vision Artificial intelligence business Image retrieval |
| Zdroj: | Storage and Retrieval for Image and Video Databases (SPIE) |
| ISSN: | 0277-786X |
| Popis: | Currently, much work has been done in the area of image storage and retrieval. However, the overall performance has been far from practical. A highly integrated wavelet-based image management system is proposed in this paper. By integrating wavelet-based solutions for image compression and decompression, content-based retrieval and progressive transmission, much higher performance can be achieved. The multiresolution nature of the wavelet transform has been proven to be a powerful tool to represent images. The wavelet transform decomposes the image into a set of subimages with different resolutions. From here three solutions for key aspects of image management are reached. The content-based image retrieval (CBIR) features of our system include the color, contour, texture, sample, keyword and topic information of images. The first four features can be naturally extracted from the wavelet transform coefficients. By scoring the similarity of users' requests with images in the database, those who have higher scores are noted and the user receives feedback. Image compression and decompression. Assuming that details at high resolution and diagonal directions are less visible to the human eye, a good compression ratio can be achieved. In each subimage, the wavelet coefficients are vector quantized (VQ), using the LGB algorithm, which is improved in our approach to accelerate the process. Higher compression ratio can be achieved with DPCM and entropy coding method applied together. With YIQ representation, color images can also be effectively compressed. There is a very low load on the network bandwidth by transmitting compressed image data across the network. Progressive transmission is possible by employment of the multiresolution nature of the wavelet, which makes the system respond faster and the user-interface more friendly. The system shows a high overall performance by exploring the excellent features of wavelet, and integrating key aspects of image management. An image retrieval service using Java is also available on the world wide web (www), to demonstrate the system's features.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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