Wavelet-based Image Compression Research Articles

Effectiveness of Low-Numerical Rank Approximation to Image Compression in Wavelet Domain

In this paper, we report a modeling of approximation for images by finding numerical rank in the wavelet domain through singular value decomposition of approximation coefficients. Firstly, the digital image is transformed into the frequency domain. Then high-frequency sub-bands are quantized to zero. This is quite obvious in wavelet-based image compression. Simultaneously, the low-frequency sub-bands are compressing by using truncated singular value decomposition (TSVD) through a numerical rank. Finally, reconstruct the approximation matrix via inverse discrete wavelet transform with low computational intricacy. This mathematical model is more adequate for solving engineering problems arises in digital image processing such as the transmission of image (reducing the bandwidth size of a communication channel) and storage capacity (space saving). The simulation results on gray and color images show that there is a gain in: (i) the compression ratio with acceptable visual quality as per human vision system; (ii) balancing of performance measures over conventional SVD methods.

Learning-Based Scalable Image Compression With Latent-Feature Reuse and Prediction

Recently, learning-based image compression model has attracted much attention due to its impressive performance and ease of optimization, compared with traditional DCT and wavelet-based image compression standards. Most learning-based image compression models are trained to minimize joint rate-distortion (RD) loss on one single RD trade-off point. However, in many multimedia applications, due to communication constraints, or display adaptation needs for different spatial formats, bit rates or power, it is necessary to provide a variety of image versions for different client devices. To fulfill this requirement, typical end-to-end image compression methods have to compress an image into several bit streams independently by a number of pre-trained networks, which are resource-consuming because of redundancy among these streams. To address this problem, inspired by traditional scalable video coding framework, we propose a learning-based end-to-end quality and spatial scalable image compression (QSSIC) model in multi-layer structure, in which each layer could generate one bitstream corresponding to a specified resolution and image fidelity. This scalability is achieved by exploring the potential of feature-domain representation prediction and reuse. To be specific, firstly, bitstreams of previous layers are used to predict the current layer representations which contains the enhancement information, and then only prediction residuals need to be coded in enhancement layers. Secondly, previous bitstreams are reused in image reconstruction in higher layers to provide basic information. The proposed model could be optimized in an end-to-end manner. Extensive experiments show that our method outperforms state-of-art deep neural networks (DNN)-based auto-encoders in simulcast scenarios. In addition, our method has a better performance than the traditional scalable image compression method scalable extension of H.264/AVC (SVC) and is comparable to scalable extension of H.265/HEVC (SHVC).

Fast Adaptive Augmented Lagrangian Digital Image Correlation

Digital image correlation (DIC) is a powerful experimental technique for measuring full-field displacement and strain. The basic idea of the method is to compare images of an object decorated with a speckle pattern before and after deformation in order to compute the displacement and strain fields. Local Subset DIC and finite element-based Global DIC are two widely used image matching methods; however there are some drawbacks to these methods. In Local Subset DIC, the computed displacement field may not satisfy compatibility, and the deformation gradient may be noisy, especially when the subset size is small. Global DIC incorporates displacement compatibility, but can be computationally expensive. In this thesis, we propose a new method, the augmented-Lagrangian digital image correlation (ALDIC), that combines the advantages of both the local (fast and in parallel) and global (compatible) methods. We demonstrate that ALDIC has higher accuracy and behaves more robustly compared to both Local Subset DIC and Global DIC. DIC requires a large number of high resolution images, which imposes significant needs on data storage and transmission. We combined DIC algorithms with image compression techniques and show that it is possible to obtain accurate displace- ment and strain fields with only 5 % of the original image size. We studied two compression techniques – discrete cosine transform (DCT) and wavelet transform, and three DIC algorithms – Local Subset DIC, Global DIC and our newly proposed augmented Lagrangian DIC (ALDIC). We found the Local Subset DIC leads to the largest errors and ALDIC to the smallest when compressed images are used. We also found wavelet-based image compression introduces less error compared to DCT image compression. To further speed up and improve the accuracy of DIC algorithms, especially in the study of complex heterogeneous strain fields at various length scales, we apply an adaptive finite element mesh to DIC methods. We develop a new h-adaptive technique and apply it to ALDIC. We show that this adaptive mesh ALDIC algorithm significantly decreases computation time with no loss (and some gain) in accuracy.

A near lossless three-dimensional medical image compression technique using 3D-discrete wavelet transform

In the field of medical image processing, there are several imaging modalities that emerged and produced high quality of images that imposes to greater amount of storage space as well as bandwidth for transmission. Therefore, there is always a great need to develop compression techniques on such images. Moreover, they produce three-dimensional images by sequence of 2D slices and process each as like 2D image. Therefore, it is essential that every medical image processing operation needs to converge on 3D space (three-dimensional image). In this paper, we provide the wavelet-based image compression technique that can be directly applied on the 3D images itself. We apply 3D DWT on a 3D volume and the resultant coefficients are taken to further compression process (like thresholding, entropy encoding). The inverse processes are performed for decompression to reconstruct the images. The effects of the results based on different wavelets are assessed and the results of lossy mode are compared in terms to lossless mode.

A near lossless three-dimensional medical image compression technique using 3D-discrete wavelet transform

In the field of medical image processing, there are several imaging modalities that emerged and produced high quality of images that imposes to greater amount of storage space as well as bandwidth for transmission. Therefore, there is always a great need to develop compression techniques on such images. Moreover, they produce three-dimensional images by sequence of 2D slices and process each as like 2D image. Therefore, it is essential that every medical image processing operation needs to converge on 3D space (three-dimensional image). In this paper, we provide the wavelet-based image compression technique that can be directly applied on the 3D images itself. We apply 3D DWT on a 3D volume and the resultant coefficients are taken to further compression process (like thresholding, entropy encoding). The inverse processes are performed for decompression to reconstruct the images. The effects of the results based on different wavelets are assessed and the results of lossy mode are compared in terms to lossless mode.

Wavelet-Based Image Compression Encoding Techniques — A Complete Performance Analysis

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).

SnowPac: a multiscale cubic B-spline wavelet compressor for astronomical images

ABSTRACT As more advanced and complex survey telescopes are developed, the size and scale of data being captured grows at increasing rates. Across various domains, data compression through wavelets has enabled the reduction of data size and increase in computation efficiency. In this paper, we provide qualitative and quantitative tests of a new wavelet-based image compression method compared against the current standard for astronomical images. The analysis is improved by making use of state-of-the-art object detection systems to accurately measure the impact of the compression. We find that a combination of lossy wavelet-based methods, efficient quantization, and lossless dictionary compressors can preserve up to 98 per cent of astronomical objects at a 10:1 compression ratio. This significant reduction in file size also preserves astronomical object properties better than existing methods. These methods help further reduce future workloads for image-heavy processing pipelines.

Implementation of an efficient FPGA architecture for capsule endoscopy processor core using hyper analytic wavelet-based image compression technique

Implementation of an efficient FPGA architecture for capsule endoscopy processor core using hyper analytic wavelet-based image compression technique

Implementation of an efficient FPGA architecture for capsule endoscopy processor core using hyper analytic wavelet-based image compression technique

To receive images of human intestine for medical diagnostics, wireless capsule endoscopy (WCE) is a state-of-the-art technology. This paper proposes implementation of efficient FPGA architecture for capsule endoscopy processor core. The main part of this processor is image compression, for which we proposed an algorithm called as hyper analytic wavelet transform (HWT). The hyper analytic wavelet transform (HWT) is quasi shift-invariant; it has a good directional selectivity and a reduced degree of redundancy. Huffman coding also used to reduce the amount of bits required to represent a string of symbols. This paper also provided forward error correction (FEC) scheme based on low density parity check codes (LDPC) to reduce bit error rate (BER) of the transmitted data. Compared to the similar existing works this paper proposed an efficient architecture.

An Efficient Pass Parallel SPIHT based Image Compression using Double Density Dual Tree Complex Wavelet Transform for WSN

For the past two decades, wavelet based image compression algorithms for Wireless Sensor Network (WSN) has gained broad attention than that of the spatial based image compression algorithms. In that, Dual Tree Complex Wavelet Transforms (DTCWT) has provided better results in terms of image quality and high compression rate. However, the selection of DTCWT based image compressions for various WSN based applications is not practically suitable, due to the major limitations of WSN such as, low bandwidth, low energy consumption and storage space. Therefore, an attempt has been made in this paper to develop image compression through simulation by considering the modified block based pass parallel Set Partitioning In Hierarchical Trees (SPIHT) with Double Density Dual Tree Complex Wavelet Transform (DDDTCWT) for compressing the WSN based images. In addition, bivariate shrink method is also adopted with the DDDTCWT to obtain better image quality within less computation time. It is observed through simulation results that above mentioned proposed technique provides better performance than that of existing compression technique

High Rate Squeezing Using Ll Band Coding In Wavelet Domain

Wavelet based image compression standards not only inspired signal and image processing community but also the research community of many research and application fields towards the wavelet theory. All wavelet based schemes follow the standard sequence of steps. They are transformation and the processing task at one end followed by the inverse of processing task and inverse transform at another end. Wavelet based compression was done in a quite different manner from its inception. The early techniques include Embedded Zerotree Wavelet (EZW) coding and Set Partitioning in Hierarchical Trees (SPIHT) coding. Although, SPIHT is an extension of EZW, both follow more or less similar process in coding and decoding. These schemes code the significant and insignificant coefficients using symbols or maintaining a list of indices of the coefficients. The decision on significant or insignificant will be taken by comparing with a threshold which will be updated in each iteration. In both the schemes, if a coefficient is identified as an insignificant one, then the bits incurred in conveying this coefficient is less and in many cases very less. One can imagine that if a coefficient is made to be an insignificant then the number of bits required will be less. This issue was taken up in this paper and bits of selected regions is chosen and a significant improvement is compression ratio is observed at a little cost of quality.

Low memory block tree coding for hyperspectral images

Hyperspectral image sensors are resource constrained and have limited on-board memory. Processing of high volume hyperspectral images pose a challenge to the memory and resources of the sensor. Contemporary wavelet based image compression schemes have intensive memory requirement of which 3D-WBTC have superior coding performance due to through the exploitation of the inter sub-band & intra sub-band redundancy. This paper presents a low memory implementation of 3D-WBTC which is a listless scheme by using the fixed size state memory to keep track of block set partitioning and significance testing of wavelet coefficients of transformed hyperspectral images. Memory access time is significantly reduced due to the elimination of lists that leads to reduced complexity. Simulation results of the proposed scheme shows that proposed coder is fast and have very low memory requirement thereby making it a suitable candidate for implementation in resource constrained hyper spectral image sensor.

Combining Image Compression with Digital Image Correlation

Digital image correlation (DIC) is a powerful experimental technique to determine displacement and strain fields. DIC methods usually require a large number of high resolution images, and this imposes significant needs on data storage and transmission. In this work, we combine digital image correlation with image compression techniques and show that it is possible to obtain accurate displacement and strain fields with only 5% of the original image size. We study two compression techniques – discrete cosine transform (DCT) and wavelet transform, and three DIC algorithms – Local Subset DIC, Global DIC and the recently proposed augmented Lagrangian DIC (ALDIC). We find that Local Subset DIC leads to the largest errors and ALDIC to the smallest when compressed images are used. We also find that wavelet-based image compression introduces less error compared to DCT image compression.

Low Complexity Hardware Architectures for Wavelet Transforms: A Survey

Presently, the major focus is on developing techniques to efficiently decrease hardware expenditure as well as hardware complications while realizing the requirements for a real-time system. The enhancement of Discrete Wavelet Transforms DWT’s hardware modelling is still a relatively novel subject of research. Such areas comprise developing an effective hardware acceleration of the implementation of the DWT of the JPEG2000 standard, to construct a practical model and to deal with the computational and communication energy limitations of the image compression system. This paper emphasizes a comprehensive survey to develop necessary solutions to enhance the potential and capacity of DWT’s computation-intensive nature algorithm implementation, particularly for low power image compression applications. The paper focuses on the major factors in order to lower the DWT principal energy consuming phase, given the energy consumption of the whole wavelet based image compression. These factors may possibly encompass some hardware-based features, such as basic coding features, low memory requirement, and low computational load. In combination with this research, other paper areas are also being investigated.

Study and analysis of wavelet based image compression techniques

This paper presented comprehensive study with performance analysis of very recent Wavelet transform based image compression techniques. Image compression is one of the necessities for such communication. The goals of image compression are to minimize the storage requirement and communication bandwidth. Compression is achieved by the removal of redundant data. Discrete Wavelet Transform (DWT) is a recently developed compression technique in image compression. DWT image compression includes decomposition (transform of image), Detail coefficients thresholding, and entropy encoding. This paper mainly describes the transform of an image using DWT and thresholding techniques. In this paper we have taken the standard image Lena of size 256X256 of 8 bit depth and applied DWT (haar). Then two results set are obtained by applying two different techniques of thresholding and then compare the result.Keywords: Discrete Cosine Transform, Wavelet Transform, JPEG Compression and Entropy Encoding

Smartphone-based quantitative measurements on holographic sensors.

The research reported herein integrates a generic holographic sensor platform and a smartphone-based colour quantification algorithm in order to standardise and improve the determination of the concentration of analytes of interest. The utility of this approach has been exemplified by analysing the replay colour of the captured image of a holographic pH sensor in near real-time. Personalised image encryption followed by a wavelet-based image compression method were applied to secure the image transfer across a bandwidth-limited network to the cloud. The decrypted and decompressed image was processed through four principal steps: Recognition of the hologram in the image with a complex background using a template-based approach, conversion of device-dependent RGB values to device-independent CIEXYZ values using a polynomial model of the camera and computation of the CIEL*a*b* values, use of the colour coordinates of the captured image to segment the image, select the appropriate colour descriptors and, ultimately, locate the region of interest (ROI), i.e. the hologram in this case, and finally, application of a machine learning-based algorithm to correlate the colour coordinates of the ROI to the analyte concentration. Integrating holographic sensors and the colour image processing algorithm potentially offers a cost-effective platform for the remote monitoring of analytes in real time in readily accessible body fluids by minimally trained individuals.

REGION SPECIFIC WAVELET COMPRESSION FOR 4K SURVEILLANCE IMAGES

For successful transmission of massively sequenced images during 4K surveillance operations large amount of data transfer cost high bandwidth, latency and delay of information transfer. Thus, there lies a need for real-time compression of this image sequences. In this study we present a region specific approach for wavelet based image compression to enable management of huge chunks of information flow by transforming Harr wavelets in hierarchical order.

Video Coding Technique with Multi Objective Particle Swarm Optimization and EZW

Video coding plays an important role in video transmission and storage applications. Today’s increasing order of multimedia applications led to a lot of research works in video coding in such a way that high compression ratio is achieved with the available bandwidth. Wavelet based image compression has witnessed great success in the past decade. Wavelet transform based motion compensated video codec performs better compression in order to meet the rate and distortion constraint in video transmission than the block based techniques. However, it is well known that the 2D DWT does not represent directional features of images efficiently. Lots of efforts have been put into multiscale directional representation. In this paper, video coding using directional transform DDWT is considered and its expansive nature is reduced by noise shaping algorithm. High compression ratio is achieved through the selection of optimal coefficients of DDWT using Multi Objective Particle Swarm Optimization (MOPSO) method. In this video coding technique, the objective functions of Entropy, Computation Time and Mean Square Error are considered for optimization with the constraints of bits per pixel and frame rate. The selected optimum coefficients are encoded using EZW method. The performance of the proposed method is compared with the standard 3D SPIHT coding.

Optimal Image Data Compression For Whole Slide Images

Introduction/ Background Whole slide scanning is rapidly entering routine pathology laboratories. Modern scanners enable digitization of tens or even hundreds of thousands slides each year. If all WSI images are stored permanently, hundreds of terabytes image files need to be stored. It is essential to use image storage methods that preserve the scan image quality, but also keep storage costs in a reasonable level. Aims Today all WSIs are stored with lossy compression methods using a variety of different file formats. At the practical level it is important to find an image file format, which results in small-sized image files but retaining image quality as “visually lossless”. Methods In this study we compared file formats ofHamamatsu, Aperio, and 3D-Histech scanners to standard JPEG2000 and to JPEG2000 specially optimized for brightfield histology WSIs. As for image quality readout we used standardized resolution charts, and evaluation by three pathologists who ranked the images by their visual quality, when displayed on a 4K computer monitor. Results Differences in WSI file sizes of scanned images deemed “visually lossless” were significant. If we set Hamamatsu Nanozoomer .NDPI file size (using its default “jpeg80 quality”) as 100%, the size of a “visually lossless” JPEG2000 file was only 15-20% of that. Comparisons to Aperio and 3D-Histech files (.svs and .mrxs at their default settings) yielded similar results. A further optimization of JPEG2000 was done by treating empty slide area as uniform white-grey surface, which could be maximally compressed. Using this algorithm, JPEG2000 file sizes were only half, or even smaller, of original JPEG2000. Variation was due to the proportion of empty slide area on the scan. We anticipate that wavelet-based image compression methods, such as JPEG2000, have a significant advantage in saving storage costs of scanned whole slide image. In routine pathology laboratories applying WSI technology widely to their histology material, absolute cost savings can be substantial.

English

Wavelet-based image compression algorithms have good performance and produce a rate scalable bitstream that can be decoded efficiently at several bit rates. Unfortunately, the discrete wavelet transform (DWT) has relatively high computational complexity. On the other hand, the discrete cosine transform (DCT) has low complexity and excellent compaction properties. Unfortunately, it is non-local, which necessitates implementing it as a block-based transform leading to the well-known blocking artifacts at the edges of the DCT blocks. This paper proposes a very fast and rate scalable algorithm that exploits the low complexity of DCT and the low complexity of the set partitioning technique used by the wavelet-based algorithms. Like JPEG, the proposed algorithm first transforms the image using block-based DCT. Then, it rearranges the DCT coefficients into a wavelet-like structure. Finally, the rearranged image is coded using a modified version of the SPECK algorithm, which is one of the best well-known wavelet-based algorithms. The modified SPECK consumes slightly less computer memory, has slightly lower complexity and slightly better performance than the original SPECK. The experimental results demonstrated that the proposed algorithm has competitive performance and high processing speed. Consequently, it has the best performance to complexity ratio among all the current rate scalable algorithms.