Image coding using wavelet transform and adaptive block truncation coding

This thesis presents a new image coding using wavelet transform and adaptive block truncation coding. Images are first pre-processed by the wavelet transform and then coded by the adaptive block truncation coding. Algorithms for both monochrome and color images are proposed and experimentally studied. The adaptive block truncation coding is also modified to achieve better performance. For coding monochrome images at the bit-rate region between 0.8 to 1.2 bits/pixel, the performance of the new coding is comparable to the ones of subband codings and other image codings using the wavelet transform; however, the new coding offers less computational load. The new coding also gives a good reconstruction of a color image at the bit-rate of 1.0 bit/pixel. The comparison between the new coding and the original adaptive block truncation coding is also given. The discussion on effects of a filter and a number of decomposition levels used for an implementation of the wavelet transform is included in this thesis, as well

An Improved Image Compression Algorithm Based on Daubechies- Wavelets with Arithmetic Coding

In this paper, we present image compression techniques to utilizing the visual redundancy and investigated. To effectively define and utilize image compression context for natural image is difficult problem. Inspired by recent research in the advancements of image compression techniques, we propose Daubechies-Wavelet with arithmetic coding towards the improvement over visual quality rather than spatial wise fidelity. Image compression using Daubechies-Wavelet with arithmetic coding is quite simple and good technique of compression to produce better compression results. In this image compression technique we first apply Daubechies-Wavelet transform then 2D Walsh-Wavelet transform on each kxk where (k=2n) block of the low frequency sub band. Split all values from each transformed block kxk followed by applying arithmetic coding for image compress. Index Terms-Image Compression, Daubechies-Wavelet, Arithmetic codin

Image Compression Using Tap 9/7 Wavelet Transform and Quadtree Coding Scheme

This paper is concerned with the design and implementation of an image compression method based on biorthogonal tap-9/7 discrete wavelet transform (DWT) and quadtree coding method. As a first step the color correlation is handled using YUV color representation instead of RGB. Then, the chromatic sub-bands are downsampled, and the data of each color band is transformed using wavelet transform. The produced wavelet sub-bands are quantized using hierarchal scalar quantization method. The detail quantized coefficient is coded using quadtree coding followed by Lempel-Ziv-Welch (LZW) encoding. While the approximation coefficients are coded using delta coding followed by LZW encoding. The test results indicated that the compression results are comparable to those gained by standard compression schemes

Modified Golomb-Rice Algorithm for Color Image Compression

The memory required to store the color image is more. We have reduced the memory requirements using Golomb-rice algorithm. Golomb-rice algorithm consists of the following two steps. In Golomb-Rice algorithm the first step is to compress the image using discrete wavelet transform. By using DWT compression the 8 × 8 image is converted into m × n sub-windows and it is converted into raster file format for producing m × n-1 differential data. Encoding is done by using Golomb-Rice coding.  After encoding, the process length, code word and size are calculated by using GR coding.In the second step decoding is done by GR coding based on the obtained length and code word. After that decoded image is decompressed in order to get the original image by using the inverse discrete wavelet transform.&nbsp

Performance Evaluation of Hybrid Coding of Images Using Wavelet Transform and Predictive Coding

Image compression techniques are necessary for the storage of huge amounts of digital images using reasonable amounts of space, and for their transmission with limited bandwidth. Several techniques such as predictive coding, transform coding, subband coding, wavelet coding, and vector quantization have been used in image coding. While each technique has some advantages, most practical systems use hybrid techniques which incorporate more than one scheme. They combine the advantages of the individual schemes and enhance the coding effectiveness. This paper proposes and evaluates a hybrid coding scheme for images using wavelet transforms and predictive coding. The performance evaluation is done using a variety of different parameters such as kinds of wavelets, decomposition levels, types of quantizers, predictor coefficients, and quantization levels. The results of evaluation are presented

A Novel Color Image Watermarking Scheme Based on Arnold Transform and Discrete Wavelet Transform using B Channel

The expansion of Internet has frequently increased the use of digital media publically. Digital media can be in the form such as audio, images, text and videos to the public. In this paper we have presented a Novel color image watermarking technique using two methods Arnold transform and discrete wavelet transform . The binary watermark is processed by using error correcting coding and Arnold Transform. Arnold transform is applied for the best scrambling round and then the watermark is embedded in the Blue color channel into the Cover image. Discrete wavelet transform method is applied on the cover image for further security. The proposed method concludes to give better results compared to the simple DWT method in image sharpening, smoothening, noise tolerance, rotating, cropping, etc aspects of image transformations and enhancement. DOI: 10.17762/ijritcc2321-8169.16040

Reliable and Efficient coding Technique for Compression of Medical Images based on Region of Interest using Directional Filter Banks

Medical images carry huge and vital information. It is necessary to compress the medical images without losing its vital-ness. The proposed algorithm presents a new coding technique based on  image compression using contourlet transform used in different modalities of medical imaging. Recent reports on natural image compression have shown superior performance of contourlet transform, a new extension to the wavelet transform in two dimensions using nonseparable and directional filter banks. As far as medical images are concerned the diagnosis part (ROI) is of much important compared to other regions. Therefore those portions are segmented from the whole image using  fuzzy C-means(FCM) clustering technique. Contourlet transform is then applied to ROI portion which performs Laplacian Pyramid(LP) and Directional Filter Banks. The region of less significance are compressed using Discrete Wavelet Transform and finally modified embedded zerotree wavelet algorithm is applied which uses six symbols instead of four symbols used in Shapiro’s EZW to the resultant image which shows better PSNR and high compression ratio.Â

Image Compression using Discrete Cosine Transform & Discrete Wavelet Transform

Image Compression addresses the problem of reducing the amount of data required to represent the digital image. Compression is achieved by the removal of one or more of three basic data redundancies: (1) Coding redundancy, which is present when less than optimal (i.e. the smallest length) code words are used; (2) Interpixel redundancy, which results from correlations between the pixels of an image & (3) psycho visual redundancy which is due to data that is ignored by the human visual system (i.e. visually nonessential information). Huffman codes contain the smallest possible number of code symbols (e.g., bits) per source symbol (e.g., grey level value) subject to the constraint that the source symbols are coded one at a time. So, Huffman coding when combined with technique of reducing the image redundancies using Discrete Cosine Transform (DCT) helps in compressing the image data to a very good extent. The Discrete Cosine Transform (DCT) is an example of transform coding. The current JPEG standard uses the DCT as its basis. The DC relocates the highest energies to the upper left corner of the image. The lesser energy or information is relocated into other areas. The DCT is fast. It can be quickly calculated and is best for images with smooth edges like photos with human subjects. The DCT coefficients are all real numbers unlike the Fourier Transform. The Inverse Discrete Cosine Transform (IDCT) can be used to retrieve the image from its transform representation. The Discrete wavelet transform (DWT) has gained widespread acceptance in signal processing and image compression. Because of their inherent multi-resolution nature, wavelet-coding schemes are especially suitable for applications where scalability and tolerable degradation are important. Recently the JPEG committee has released its new image coding standard, JPEG-2000, which has been based upon DWT

Lossy To Lossless Medical Image Coding Using Joint Bit Scanning Method

A new algorithm for progressive medical image coding is presented. On the 8-bit gray scale image, lifting based integer wavelet transform (IWT) are applied to get the three level multi-resolution Integer wavelet transformed image. Then, it is encoded using block based partitioning scheme to exploit the energy clustering in frequency and in space. Whenever a pixel is found significant, pixel value is completely transmitted using vertical bit scanning and then proceeds again with block based coding. Experiments are carried on MRI images to prove the effectiveness of the proposed algorithm. The results shows a significant improvement in terms of distortion measured as peak signal to noise ratio (PSNR) and Correlation Coefficient (CoC)  for a given bit rate compared to the existing state of the art embedded image coding methods

On the design of fast and efficient wavelet image coders with reduced memory usage

Image compression is of great importance in multimedia systems and applications because it drastically reduces bandwidth requirements for transmission and memory requirements for storage. Although earlier standards for image compression were based on the Discrete Cosine Transform (DCT), a recently developed mathematical technique, called Discrete Wavelet Transform (DWT), has been found to be more efficient for image coding. Despite improvements in compression efficiency, wavelet image coders significantly increase memory usage and complexity when compared with DCT-based coders. A major reason for the high memory requirements is that the usual algorithm to compute the wavelet transform requires the entire image to be in memory. Although some proposals reduce the memory usage, they present problems that hinder their implementation. In addition, some wavelet image coders, like SPIHT (which has become a benchmark for wavelet coding), always need to hold the entire image in memory. Regarding the complexity of the coders, SPIHT can be considered quite complex because it performs bit-plane coding with multiple image scans. The wavelet-based JPEG 2000 standard is still more complex because it improves coding efficiency through time-consuming methods, such as an iterative optimization algorithm based on the Lagrange multiplier method, and high-order context modeling. In this thesis, we aim to reduce memory usage and complexity in wavelet-based image coding, while preserving compression efficiency. To this end, a run-length encoder and a tree-based wavelet encoder are proposed. In addition, a new algorithm to efficiently compute the wavelet transform is presented. This algorithm achieves low memory consumption using line-by-line processing, and it employs recursion to automatically place the order in which the wavelet transform is computed, solving some synchronization problems that have not been tackled by previous proposals. The proposed encodeOliver Gil, JS. (2006). On the design of fast and efficient wavelet image coders with reduced memory usage [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1826Palanci