An Algorithm with Low Complexity for Image Compression and its Hardware Implementation using VHDL

Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as Wavelet Neural Networks (WNN) and genetic algorithms are being developed to explore the future of image coding. Successful applications of WNN in the case of function approximation become well established. This paper presents a direct solution method based on Multi Library WNN for color image compression and coding which consists to transform an RGB image into Luminance-Chrominance space and then segment the luminance in a set of m blocks n by n pixels. These blocks should be transferred row by row (1D input vector) to the input of our wavelet network. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.

For best performance in image compression, wavelet transforms require filters that combine some important properties, such as orthogonality and symmetry. However, in the designing of wavelets this combination is limited because to have these simultaneous properties is not possible, except in the Haar case. The multiwavelet system allows more design options than wavelet, and therefore, several simultaneous characteristics such as: compact support, smoothness, higher order approach, symmetry and orthogonality, among others. Therefore, establishing parameters which allow comparing of scalar wavelets and multiwavelets performance, is a necesity. This paper provides an overview of filter banks, wavelets, multiwavelets, basic principles, and connections between each one, in addition to some experimental results from the compression of test images, comparing each system performance.

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

Wavelet is one method that can be used as a step to recognition an individual . Wavelet is used to perform an image feature extraction . In the process of image texture analysis , showed the value of the coefficients of a wavelet . The magnitude of the coefficients obtained using wavelet feature extraction is influenced by several factors . Besides affected by the type of wavelet used , is also influenced by the magnitude of the wavelet decomposition level of itself. In the wavelet is known as the wavelet energy retention , which means the number of retained energy after undergoing a process of decomposition and cutting coefficients . During the decomposition process , the calculation for texture analysis is often a constraint . In order for the current calculation lighter texture analysis , necessary to the process of cutting coefficients based on the retention factor of the wavelet coefficients . Based on these issues , created a program to analyze the influence of variations in the level of wavelet decomposition and coefficient of variation coefficient of cutting a cut on the image . The object of this final project is 30 iris image that has been converted into polar form presented . Having experienced the process of cutting coefficients , to prevent the image of the reverse transformation has a big difference to the original image , in this study was calculated Mean Square Error ( MSE), Peak Signal to Noise Ratio ( PSNR), and Euclidean distance to determine the level of similarity of image input and output images . The Highest values of Retention ​, MSE and the Euclidean distance is obtained at the level of decomposition of 1 and the lowest at the level of decomposition 6 . While the truncated coefficient and PSNR values ​​obtained at the highest level of decomposition of 6 and the lowest level of decomposition of 6 . The variation coefficient of pieces , value retention and highest PSNR obtained at koefsien piece 5 and the lowest coefficient of 50 pieces . While the coefficient value is truncated , MSE and the highest Euclidean distance is obtained at koefsien pieces 50 and the lowest coefficient of 5 pieces . Keywords: iris, texture analysis, Haar wavelet transform, MSE, PSNR, E uclidean distance

Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image compression depend on the concept of coding set of wavelet coefficients as zero trees. The proposed compression algorithm that combined dual image compression techniques (DICT) invest an excellent features from each methods, which then produce promising technique for still image compression through minimize bits number that required to represent the input image, to the degree allowed without significant impact on quality of reconstructed image.
 The experimental results present that DICT will improve the image compression efficiency between 8 to 24%, and will result high performance of metrics values.

Image compression is a significant technology in the transmission and storage of digital images. The importance of image compression is that to preserve the visual feature of the image. One of the finest image compression techniques is using wavelet transform which have comparatively had several benefits over other. Basically wavelets are confined in both time and frequency. Wavelet transformations perform multilevel decomposition of image that is diving image into sub parts of image at different measures. Discrete wavelet transform examined the signal at a different frequency bandwidth that is approximate band and detail band. Using wavelet with balance resolution at any time and frequency gives a better signal illustration using multilevel analysis. Therefore, at very low bit rate the suggested method improves the visual feature of decoded image.

Focused on the image data storage, compression and transmission of mars rover, system design and key hardware/software architecture design are carried out in this paper based on the analysis of function requirements and key design features. Image compression algorithm and transmission protocol are proposed, and the image storage and compression module in Mars rover is designed and implemented. The multi-camera partition parallel image file system based on FPGA（Field Programmable Gate Array）is realized. The adaptive first 1 run length coding algorithm for image compression based on DSP is realized on this module. Finally the function of multi-camera image data storage and management, flexible control of image compression ratio, quality progressive transmission, coding of region of interest, anti-error code diffusion and image thumbnail downward transmission are realized in the engineering development, meeting the reliable, efficient and flexible application requirements of China’s first Mars exploration mission.

With advancement of technology, the image processing techniques play an important role. Image processing is required to improve the pictorial information for human perception and for autonomous machine applications. It finds application in various fields such as quality control, remote sensing, imaging science, etc. It also finds application in areas where efficient storage and transmission of images is necessary, Say for example; we want to store an image in the computer with less disk space. Also, in cases where we want to signal an image or video on a transmission medium with less bandwidth communication channel. Images are usually in the form of matrices and an uncompressed image uses huge number of bytes for storage. Image compression is used to reduce the size of the bitmap without compromising on the quality of an image. This enables to store large number of images in the same given amount of storage space. Image compression will also reduce the time taken to transmit or to download images from the internet. Many methods are available to compress an image file. The image compression methods which have gained popularity are the transform based coding methods like Discrete Cosine Transform (DCT), discrete wavelet Transform (DWT) and fractals. However these methods have drawbacks like low compression ratio and high encoding time. In this paper a new hybrid methodology is proposed by combining lossy and lossless compression methods. The proposed hybrid technique combines DCT and fractal quadtree decomposition with Huffman encoding of threshold value 0.2. The results obtained from the proposed method are then compared with state-of-the-art compression methods.

In the era of intelligent media, we all need high quality image or video information. However, due to the limitation of bandwidth or storage resource, we usually use low bit rate coding technology for image compression. But the image distortion caused will bring in a visually inferior experience, and the actual information obtained is often not sufficient. Therefore, the super-resolution reconstructed image coding technology is researched to provide a higher dynamic bit rate variation range. Based on image super-resolution reconstruction, this paper proposes two targeted image compression coding frameworks. Firstly, we compress the DCT down-sampled image as the input of super-resolution reconstruction, and use image completion as the pretreatment process of image super-resolution reconstruction, which effectively improves the quality of reconstructed image at low bit rate and, hence improves the coding efficiency. Secondly, a new image compression technique is proposed here which is based on residual compensation and super-resolution reconstruction. In the encoder, the super-resolution reconstructed image is subtracted from the original image to obtain the residual image which is compressed. In the decoder, the residual image is decompressed and added to the reconstructed image to complete the decoding process which achieves flexible rate control.

Because of the high dimensionality of hyperspectral images, traditional statistics-based classifiers cannot be directly used for the hyperspectral image classification with limited training samples. The commonly used method to solve this problem is dimensionality reduction. Several research works have proven that wavelet transform provides an appropriate and effective tool for spectral feature extraction. Also, the researches about back-propagation neural networks for image classification have been investigated. In this study, wavelet networks is proposed for hyperspectral image classification. With wavelet networks, the important task of feature extraction is performed with wavelet transform, whereas classification is carried out by back-propagation neural networks. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The experiment results suggested that wavelet networks can be a useful and effective classifier for hyperspectral image classification.

In the recent growth of data intensive and multimedia based applications, efficient image compression solutions are becoming critical. The main objective of Image Compression is to reduce redundancy of the data and improve the efficiency. The main techniques used are Fourier Analysis, Discrete Cosine Transform vector quantization method, sub-band coding method. The drawbacks in the above methods are, they cannot be used for real time systems. In order to overcome these problems, the Wavelet Transform method has been introduced. Wavelet Analysis is highly capable of revealing aspects of data like trends, breakdown points, discontinuities in higher derivates and self similarity and can often compress or diagnose a signal without appreciable degradation. Here, we implement a lossy image compression technique using Matlab Wavelet Toolbox and Matlab Functions where the wavelet transform of the signal is performed, then calculated a threshold based on the compression ratio acquired by the user.

Recent advancements in hyperspectral imaging have significantly increased the acquired data volume, creating a need for more efficient compression methods for handling the growing storage and transmission demands. These challenges are particularly critical for onboard satellite systems, where power and computational resources are limited, and real-time processing is essential. In this article, we present a novel FPGA-based hardware acceleration of a near-lossless compression technique for hyperspectral images by leveraging a division-free quadrature-based square rooting method. In this regard, the two division operations inherent in the original approach were replaced with pre-computed reciprocals, multiplications, and a geometric series expansion. Optimized for real-time applications, the synthesis results show that our approach achieves a high throughput of 1611.77 Mega Samples per second (MSps) and a low power requirement of 0.886 Watts on the economical Cyclone V FPGA. This results in an efficiency of 1819.15 MSps/Watt, which, to the best of our knowledge, surpasses recent state-of-the-art hardware implementations in the context of near-lossless compression of hyperspectral images.

The rapid advancement of digital technology has created a demand for improved compression methods. Image compression is data compression applied to digital images. The goal is to eliminate picture data redundancy in order to store or transfer data efficiently. When a picture is compressed at low bitrates, the image fidelity is lost. Images can be compressed in several ways. This paper reviews image kinds and compression strategies. All type of images and the factors by which one can identify the performance assessment of images are described in this paper.

Image compression is a basic need for the progressive transmission or storage of image data via any media. For the effective processing of image data the coding standards were developed. The most commonly used standard in current scenario is the JPEG and JPEG-2000 coding. These standards have become the industry standards and are progressively been in use for almost all image processing applications. In the process of image coding various sub-processing operations occur which process the image blocks by transformation, quantization and encoding before transmitting or storing. In the process, quantization is observed to be a time consuming and is needed to be processed in high precision so as to achieve less processing error and also in reasonable time. The process of quantization has shown greater efficiency in vector quantization than earlier methods. Vector quantization is observed to be an efficient block based lossy compression technique. Due to simpler coding process and high compression ratio VQ are widely used. Predictive vector Quantization (PVQ) is commonly used memory based vector Quantization method. The main drawback with PVQ is the high computational complexity which limits PVQ to use in real time applications. Shorter coding time can be archived by reducing the number of code words or instructions, but the reconstructed image quality will degraded in the case of fewer code words. Hence, it is necessary to propose fast coding algorithms for PVQ. In this paper a modified version of the PVQ approach has been suggested in order to process faster, which ensue to be a compatible mechanism for real time image coding in JPEG based coding system. Keywords: Compression, quantization, PVQ encoder, mean-square-error, Discrete Cosine Transform.

This work includes some new aspects of motion estimation by the optic flow method in scale spaces. The usual techniques for motion estimation are limited to the application of coarse to fine strategies. The coarse to fine strategies can be successful only if there is enough information in every scale. In this work we investigate the motion estimation in the scale space more basically. The wavelet choice for scale space decomposition of image sequences is discussed in the first part of this work. We make use of the continuous wavelet transform with rotationally symmetric wavelets. Bandpass decomposed sequences allow the replacement of the structure tensor by the phase invariant energy operator. The structure tensor is computationally more expensive because of its spatial or spatio-temporal averaging. The energy operator needs in general no further averaging. The numerical accuracy of the motion estimation with the energy operator is compared to the results of usual techniques, based on the structure tensor. The comparison tests are performed on synthetic and real life sequences. Another practical contribution is the accuracy measurement for motion estimation by adaptive smoothed tensor fields. The adaptive smoothing relies on nonlinear anisotropic diffusion with discontinuity and curvature preservation. We reached an accuracy gain under properly chosen parameters for the diffusion filter. A theoretical contribution from mathematical point of view is a new discontinuity and curvature preserving regularization for motion estimation. The convergence of solutions for the isotropic case of the nonlocal partial differential equation is shown. For large displacements between two consecutive frames the optic flow method is systematically corrupted because of the violence of the sampling theorem. We developed a new method for motion analysis by scale decomposition, which allows to circumvent the systematic corruption without using the coarse to fine strategy. The underlying assumption is, that in a certain neighborhood the grey value undergoes the same displacement. If this is fulfilled, then the same optic flow should be measured in all scales. If there arise inconsistencies in a pixel across the scale space, so they can be detected and the scales containing this inconsistencies are not taken into account.