Analysis-by-Synthesis-based Quantization of Compressed Sensing Measurements

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods.Comment: 5 pages, Published in ICASSP 201

Multimodal decision-level fusion for person authentication

In this paper, the use of clustering algorithms for decision-level data fusion is proposed. Person authentication results coming from several modalities (e.g., still image, speech), are combined by using fuzzy k-means (FKM), fuzzy vector quantization (FVQ) algorithms, and median radial basis function (MRBF) network. The quality measure of the modalities data is used for fuzzification. Two modifications of the FKM and FVQ algorithms, based on a novel fuzzy vector distance definition, are proposed to handle the fuzzy data and utilize the quality measure. Simulations show that fuzzy clustering algorithms have better performance compared to the classical clustering algorithms and other known fusion algorithms. MRBF has better performance especially when two modalities are combined. Moreover, the use of the quality via the proposed modified algorithms increases the performance of the fusion system

An efficient technique of texture representation in segmentation-based image coding schemes

In segmentation-based image coding techniques the image to be compressed is first segmented. Then, the information is coded describing the shape and the interior of the regions. A new method to encode the texture obtained in segmentation-based coding schemes is presented. The approach combines 2-D linear prediction and stochastic vector quantization. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved.Peer ReviewedPostprint (published version

Improvements on stochastic vector quantization of images

A novel nonadaptive fixed-rate vector quantizer encoding scheme is presented, and preliminary results are shown. The design of the codebook has been based on a stochastic approach in order to match a previously defined model for the image to be encoded. Following this approach, the generation of the codebook is made extremely simple in terms of computational load. Good visual results are shown in the range of 0.5-0.8 bit/pixel. Much better performance is expected for adaptive schemes.Peer ReviewedPostprint (published version

A new approach to texture coding using stochastic vector quantization

A new method for texture coding which combines 2-D linear prediction and stochastic vector quantization is presented in this paper. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ, using an algorithm which takes into account the pixels surrounding the block being encoded. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved.Peer ReviewedPostprint (published version