Browsing by Author "Jafarkhani, Hamid"
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Item Adaptive Wavelet Based Image Coding(1994) Jafarkhani, Hamid; Farvardin, N.; ISRNew schemes for classification of images are suggested. An application of these methods in adaptive DCT of images is considered. A new method to combine classification and bit allocation is introduces. Also, an efficient wavelet based image coding system using classification for adaptation is developed. Finally, practical considerations concerning overhead, complexity and performance are discussed.Item Channel--Matched Hierarchical Table--Lookup Vector Quantization for Transmission of Video Over Wireless Channels(1997) Jafarkhani, Hamid; Farvardin, Nariman; ISRWe propose a channel-matched hierarchical table-lookup vector quantizer (CM-HTVQ) which provides some robustness against the channel noise. We use a finite-state channel to model slow fading channels and propose an adaptive coding scheme to transmit a source over wireless channels. The performance of CM-HTVQ is in general slightly inferior to that of, channel-optimized vector quantizer (COVQ) (the performances coincide at some cases); however, the encoder complexity of CM-HTVQ is much less than the encoder complexity of COVQ. A copy of this report has been published in the proceedings of The 1st Annual Advanced Telecommunications/Information Distribution Research Program Conference, January 21-22, 1997.Item Fast Reconstruction of Subband-Decomposed Progressively- Transmitted, Signals(1997) Jafarkhani, Hamid; Farvardin, Nariman; ISRIn this paper we propose a fast reconstruction method for a progressive subband-decomposed signal coding system. It is shown that unlike the, normal approach which contains a fixed computational complexity, the, computational complexity of the proposed approach is proportional to the number of refined coefficients. Therefore, using the proposed approach in image coding applications, we can update the image after receiving each new coefficient and create a continuously refined perception. This can be done without any extra computational cost compared to the normal case where, the image is reconstructed after receiving a predefined number of bits.- A copy of this report has been published in the proceedings of
The 1st Annual Advanced Telecommunications/Information Distribution Research Program Conference, January 21-22, 1997.Item Wavelet Coding of Images: Adaptation, Scalability, and Transmission over Wireless Channels(1997) Jafarkhani, Hamid; Farvardin, N.; ISRIn this dissertation, we study the problem of image compression for storage and transmission applications separately. In addition to proposing new image coding systems, we consider different design constraints such as complexity and scalability.We propose a new classification scheme, dubbed spectral classification, which uses the spectral characteristics of the image blocks to classify them into one of a finite number of classes. The spectral classifier is used in adaptive image coding based on the discrete wavelet transform and shown to outperform gain-based classifiers while requiring a lower computational complexity. The resulting image coding system provides one of the best available rate-distortion performances in the literature. Also, we introduce a family of multiresolution image coding systems with different constraints on the complexity. For the class of rate-scalable image coding systems, we address the problem of progressive transmission and propose a method for fast reconstruction of a subband-decomposed progressively transmitted image.
Another important problem studied in this dissertation is the transmission of images over noisy channels, especially for the wireless channels in which the characteristics of the channel is time-varying. We propose an adaptive rate allocation scheme to optimally choose the rates of the source coder and channel coder pair in a tandem source-channel coding framework. Also, we suggest two adaptive coding systems for quantization and transmission over a finite-state channel using a combined source and channel coding scheme. Finally, we develop simple table- lookup encoders to reduce the complexity of channel-optimized quantizers while providing a slightly inferior performance. We propose the use of lookup tables for transcoding in heterogeneous networks