Showing papers by "Tanuja Sarode published in 2008"

An Efficient Fast Algorithm to Generate Codebook for Vector Quantization

Abstract: In this paper we present a very simple and yet effective algorithm to generate codebook. The algorithm uses sorting method to generate codebook and the codevectors are obtained by using median approach. The proposed algorithm was experimented on six different images each of size 512 x 512 and four different codebooks of sizes 128, 256, 512 and 1024 are generated. The proposed algorithm is found to be much faster than the LBG and KPE algorithm. The performance of this algorithm is better than LBG and KPE algorithms considering MSE, PSNR and execution time. The proposed algorithm gives less MSE as compared to the LBG for the codebooks of sizes 128, 256, 512 & 1024 respectively. It also gives higher PSNR as compared to LBG for the codebooks of various sizes.

Speech Data Compression using Vector Quantization

Abstract: Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table shows computational complexity of these three algorithms. Here we have introduced a new performance parameter Average Fractional Change in Speech Sample (AFCSS). Our FCG algorithm gives far better performance considering mean absolute error, AFCSS and complexity as compared to others. Keywords—Vector Quantization, Data Compression, Encoding,, Speech coding.

Color Image Segmentation Using Kekre-s Algorithm for Vector Quantization

Abstract: In this paper we propose segmentation approach based on Vector Quantization technique. Here we have used Kekre’s fast codebook generation algorithm for segmenting low-altitude aerial image. This is used as a preprocessing step to form segmented homogeneous regions. Further to merge adjacent regions color similarity and volume difference criteria is used. Experiments performed with real aerial images of varied nature demonstrate that this approach does not result in over segmentation or under segmentation. The vector quantization seems to give far better results as compared to conventional on-the-fly watershed algorithm. Keywords—Image Segmentation,, Codebook, Codevector, data compression, Encoding

Grid Based Image Scaling Technique

Abstract: The growing interest in image scaling is mainly due to the availability of digital imaging devices such as, digital video cameras, digital camcorders, 3G mobile handsets, high definition monitors etc. Scaling a digital image is a demanding and very important area of research. The grid based image scaling technique is proposed, where grid size equals the new desired dimensions of digital image. Virtually the grid is placed on the image and the intensity value of each pixel of grid is computed by taking weighted average of all intensities which are part of the imaginary pixel. The proposed grid based scaling algorithm outperforms other standard and widely used scaling techniques like linear interpolation, B-spline with order 2, B-spline with order 3. The algorithm is capable of scaling both grey-scale and color images of any resolution in any scaling factor. MSE and PSNR comparisons for down-after-up image scaling proves that the proposed technique gives better quality scaled images over other methods.