Browsing by Subject "Image transmission"
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Item A study of measurements of blocking defects in highly-compressed images(Texas Tech University, 1997-12) Zhong, Jianqiang NormanNot availableItem Error-resilient schemes for efficient transmission of Embedded Wavelet Coded Images(Texas Tech University, 2005-12) Sriraja, Yagneswaran; Karp, Tanja; Sinzinger, Eric D.; Nutter, Brian; Mitra, SunandaThe reliable transmission of compressed images across noisy transmission channels and networks prone to packet losses is considered. The problem is viewed from a source coding perspective, where the main constraints are maximum coding efficiency and minimum redundancy. Error-resilient mechanisms are incorporated into wavelet-based image encoding schemes to obtain robust data streams that withstand significant bit errors and packet losses. The robust encoder requires no additional redundancy and shows coding improvements of 0.5-1.5 dB in SNR values over other similar methods. Redundant sets of wavelet coefficients generated by overcomplete discrete wavelet transform are used as side information to improve error-resilience at high packet loss rates. The implementations are flexible and can be easily adapted to any type of transmission channel with different network parameters.Item Error-resilient schemes for efficient transmission of embedded wavelet coded images(2005-12) Sriraja, Yagneswaran; Karp, Tanja; Sinzinger, Eric D.; Nutter, Brian; Mitra, SunandaThe reliable transmission of compressed images across noisy transmission channels and networks prone to packet losses is considered. The problem is viewed from a source coding perspective, where the main constraints are maximum coding efficiency and minimum redundancy. Error-resilient mechanisms are incorporated into wavelet-based image encoding schemes to obtain robust data streams that withstand significant bit errors and packet losses. The robust encoder requires no additional redundancy and shows coding improvements of 0.5-1.5 dB in SNR values over other similar methods. Redundant sets of wavelet coefficients generated by overcomplete discrete wavelet transform are used as side information to improve error-resilience at high packet loss rates. The implementations are flexible and can be easily adapted to any type of transmission channel with different network parameters.Item Fast and efficient progressive image coding and transmission using wavelet decomposition(Texas Tech University, 1999-05) Sharma, MohitWith the recent boom in multimedia and the Iniernel. miage compression and techniques for progressive image transmission have become quite important. This thesis describes the concept and design of a codec for progressixc image transmission highlighted by a new technique SPHIT (Set Partitioning in Hierarchical Trees). This technique works on the principles of partial ordering by magnitude utilizing a sci partitioning sorting algorithms, ordered bit plane transmission, and exploitation of selfsimilarity across different scales of an image wavelet transform. The said principles of SPIHT are no different than what was described in the original EZW by J. P. Shapiro. But the approach for implementation of SPIHT is significantly different. Here the ordering information for image data is not explicitly transmitted. Instead, the fact that the execution path of any algorithm is defined by the results of the comparisons on its branching points is exploited to obtain ordering information at the decoder. The decoder and the encoder not only share the same sorting algorithm, but also the same execution path. Thus, the decoder can recover the ordering information from its execution path, which happens to be identical to that of the encoder. An attempt to highlight the basic differences between the EZW and SPIHT is made by taking an example of 8 x 8 image section.Item Image recovery and segmentation using competitive learning in a computational network(Texas Tech University, 1992-12) Phoha, Vir ViranderIn this study, the principle of competitive learning is used to develop an iterative algorithm for image recovery and segmentation, within the framework of Markov Random Fields (MRF). the image recovery problem is transformed to the problem of minimization of an energy function. A local update rule for each pixel point is then developed in a stepwise fashion and is shown to be a gradient descent rule for an associated global energy function. Relationship of the update rule to Kohonens update rule is shown. Quantitative measures of edge preservation and edge enhancement for synthetic images are introduced. Simulation experiments using this algorithm on real and synthetic images show promising results on smoothing within regions and also on enhancing the boundaries. Restoration results are compared with recently published results using the mean field approximation. Segmentation results using the proposed approach are compared with edge detection using fractal dimension, edge detection using mean field approximation, and edge detection using the Sobel operator. Edge points obtained by using these techniques are combined to produce edge maps which include both hard and soft edges.