Comparing Vector Quantization and Wavelet Coefficients Essays

Table I displays six solid binary images along with their corresponding compression ratios. On considering Run-Length coding, the alternative encoding scheme produces better performance than the other encoding scheme. On an average, the proposed algorithm performs well on comparison with the standard Huffman coding algorithm by approximately 1.58%.

This division can be performed up to many levels. It captures not only notion of frequency content but also temporal content. Wavelets provide a mathematical way of encoding information in such a way that it is layered according to level of detail. DWT is more computationally efficient than other transformations because of its excellent localization properties. Wavelets are capable of complete lossless reconstruction of image[6].

In this report I will be discussing the different file formats, compression techniques, image resolution and colour depth. I will be explaining the different purposes, then, after I have issued an in-depth explanation of image quality and file size I will be completing a final conclusion about the best ones to use for certain tasks.

The lifting scheme is a useful way of looking at discrete wavelet transform. It is easy to understand, since it performs all operations in the time domain, rather than in the frequency domain, and has other advantages as well. This section illustrates the lifting approach using the Haar Transform .

Researchers published different ways to compute the parameters for the thresholding of wavelet coefficients. Data adaptive thresholds were introduced to achieve optimum value of threshold. Later efforts found that substantial improvements in perceptual quality could be obtained by translation invariant methods based on thresholding of an Undecimated Wavelet Transform . These thresholding techniques were applied to the nonorthogonal wavelet coefficients to reduce artifacts. Multiwavelets were also used to achieve similar results. Probabilistic models using the statistical properties of the wavelet coefficient seemed to outperform the thresholding techniques and gained ground. Recently, much effort has been devoted to Bayesian denoising in Wavelet domain. Hidden Markov Models and Gaussian Scale Mixtures have also become popular and more research continues to be published. Tree Structures ordering the wavelet coefficients based on their magnitude, scale and spatial location have been researched. Data adaptive transforms such as Independent Component Analysis (ICA) have been explored for sparse shrinkage. The trend continues to focus on using different statistical models to model the statistical properties of the wavelet coefficients and its neighbors. Future trend will be towards finding more accurate probabilistic models for the distribution of non-orthogonal wavelet

The developed watermarking technology embeds two watermarks, a strong direct-sequence spread spectrum (SS) watermark tiled over the image in the lapped bi-orthogonal transform (LBT) domain [5]. This watermark only signals the existence of the meta-data. Next, we embed the meta-data bits using a regional statistic quantization method. The quantization noise is optimized to improve the strength of the SS watermark while obeying the constraints imposed by the perceptual model. We built the watermarks to be particularly robust to aggressive JPEG compression and

Conventional cryptographic algorithms, which are generally aimed at encrypting like text data (known as Naïve approach) not well suited for video encryption. This is due to the fact that conventional cryptographic algorithms cannot process the large volume of video data in real-time. Selective encryption of the H.264/AVC bitstream puts decoder to fully decode the encrypted video with degradation in video quality. This perceptual encryption has low encryption and decryption time. The video data are compressed to reduce the storage space required and to save bandwidth for transmission. Video compression removes redundancy, so that it is difficult to guess about a portion of a compressed bitstream to another. In addition,

The goal of this method is to find the correlated pixel within a certain disparity range that minimizes the associated error and maximizes the similarity.

Abstract: Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are the most known methods used in digital image compression. Wavelet transform has better efficiency compared to Fourier transform because it describe any type of signals both in time and frequency domain simultaneously. In this paper, we will discuss the use of Discrete Cosine Transform (DCT) and Discrete wavelet transformation (DWT) based Image compression Algorithmand compare the efficiency of both methods. We do the numerical experiment by considering various types of images and by applying DCT and DWT-SPIHT to compress an image. We found that DWT yields better result as compared to DCT.

The wavelet based image coder would generally begin with the process of transformation of image data from one domain to another domain taking example of a FDCT(Forward Discrete transform) nothing but a discrete time version of a fourier cosine series. This process would not involve any losses since it deals with just transformation. The transform must decorrelate in such a manner no important information of signal is lost. These signals need not be encoded here as they are just being transformed the main process of compression has to be taken place in the next stage. After the signals have been transformed the signal have to be quantized making use of quantisation table wherein at the decoder end these quantization values are multiplied with these signals to retreive the original or reconstructed information signal. The main process happens at tthis stage called as compression. The inverse transform does the process of reconstruction the original signal. The process of quantisation is not invertible and hence the original

Abstract: The original digital speech signal contains tremendous measure of memory, the main concept for the speech compression algorithm is presented here, in which bit rate of the speech signal is reduced with maintaining signal quality for storage, memory saving or transmission over the long distance. The concentration of this project is to compact the digital speech signal using Discrete Wavelet Transform and reconstruct same signal using inverse transform, in .NET. The algorithm of Compression is oriented in three basic operation, they are apply the DWT, Threshold, Encode the signal for transmission Analysis of compression procedure is done by comparing the original speech and reconstructed signal. The main advantages of DWT provides variable compression factor.

Abstract---In this paper new image compression techniques are presented by using existing techniques such as Singular value decomposition, wavelet difference reduction (WDR) and Adaptive wavelet difference reduction (ASWDR). The SVD has been taken as a standard technique to hybrid with WDR and ASWDR. Firstly SVD is combined with WDR (SVD-WDR) and after that it is combined with its advance version that is ASWDR (SVD-ASWDR) in order to achieve better image quality and higher compression rate. These two techniques are implemented or tested on several images and results are compared in terms of PSNR, MSE and CR.

Both sides of this equation are equivalent ways of expressing a digital image quantitatively. The right side is a matrix of real numbers. Each element of this matrix is called an image element, picture element, or pixel. The term pixel is used throughout the rest of this study [11].

Digital images are very large in size and occupy large storage space. They take larger bandwidth and take more time for upload and download through the internet. In order to overcome this problem various compression algorithms are used. Wavelet based image coding, such as the JPEG2000 standard, is widely used because of its high compression efficiency. There are three important wavelet-based image coding algorithms are used that have embedded coding property enabling easy bit rate control with progressive

Digital images are very large in size and occupy large storage space. They take larger bandwidth and take more time for upload and download through the internet. In order to overcome this problem various compression algorithms are used. Wavelet based image coding, such as the JPEG2000 standard, is widely used because of its high compression efficiency. There are three important wavelet-based image coding algorithms are used that have embedded coding property enabling easy bit rate control with