Adjustable compression method for still JPEG images

There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method.

Sparse image representation with encryption

In this thesis we present an overview of sparse approximations of grey level images. The sparse representations are realized by classic, Matching Pursuit (MP) based, greedy selection strategies. One such technique, termed Orthogonal Matching Pursuit (OMP), is shown to be suitable for producing sparse approximations of images, if they are processed in small blocks. When the blocks are enlarged, the proposed Self Projected Matching Pursuit (SPMP) algorithm, successfully renders equivalent results to OMP. A simple coding algorithm is then proposed to store these sparse approximations. This is shown, under certain conditions, to be competitive with JPEG2000 image compression standard. An application termed image folding, which partially secures the approximated images is then proposed. This is extended to produce a self contained folded image, containing all the information required to perform image recovery. Finally a modified OMP selection technique is applied to produce sparse approximations of Red Green Blue (RGB) images. These RGB approximations are then folded with the self contained approach.

Improving minimum rate predictors algorithm for compression of volumetric medical images

Medical imaging technologies are experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like CT or MRI. Furthermore, legal restrictions impose that these scans must be archived for several years. These facts led to the increase of storage costs in medical image databases and institutions. Thus, a demand for more efficient compression tools, used for archiving and communication, is arising. Currently, the DICOM standard, that makes recommendations for medical communications and imaging compression, recommends lossless encoders such as JPEG, RLE, JPEG-LS and JPEG2000. However, none of these encoders include inter-slice prediction in their algorithms. This dissertation presents the research work on medical image compression, using the MRP encoder. MRP is one of the most efficient lossless image compression algorithm. Several processing techniques are proposed to adapt the input medical images to the encoder characteristics. Two of these techniques, namely changing the alignment of slices for compression and a pixel-wise difference predictor, increased the compression efficiency of MRP, by up to 27.9%. Inter-slice prediction support was also added to MRP, using uni and bi-directional techniques. Also, the pixel-wise difference predictor was added to the algorithm. Overall, the compression efficiency of MRP was improved by 46.1%. Thus, these techniques allow for compression ratio savings of 57.1%, compared to DICOM encoders, and 33.2%, compared to HEVC RExt Random Access. This makes MRP the most efficient of the encoders under study.

Vision-based place recognition : how low can you go?

In this paper we use the algorithm SeqSLAM to address the question, how little and what quality of visual information is needed to localize along a familiar route? We conduct a comprehensive investigation of place recognition performance on seven datasets while varying image resolution (primarily 1 to 512 pixel images), pixel bit depth, field of view, motion blur, image compression and matching sequence length. Results confirm that place recognition using single images or short image sequences is poor, but improves to match or exceed current benchmarks as the matching sequence length increases. We then present place recognition results from two experiments where low-quality imagery is directly caused by sensor limitations; in one, place recognition is achieved along an unlit mountain road by using noisy, long-exposure blurred images, and in the other, two single pixel light sensors are used to localize in an indoor environment. We also show failure modes caused by pose variance and sequence aliasing, and discuss ways in which they may be overcome. By showing how place recognition along a route is feasible even with severely degraded image sequences, we hope to provoke a re-examination of how we develop and test future localization and mapping systems.

vSpeak: Edge detection based feature extraction for sign to text conversion

This paper presents 'vSpeak', the first initiative taken in Pakistan for ICT enabled conversion of dynamic Sign Urdu gestures into natural language sentences. To realize this, vSpeak has adopted a novel approach for feature extraction using edge detection and image compression which gives input to the Artificial Neural Network that recognizes the gesture. This technique caters for the blurred images as well. The training and testing is currently being performed on a dataset of 200 patterns of 20 words from Sign Urdu with target accuracy of 90% and above.

分形图像压缩方法研究的新进展

分形图像压缩是一种利用迭代函数系统理论(IFs)、基于自相似特征的有损编码方法。它以其高压缩比的潜在性能而在近年来倍受重视，但目前实现自动IFs编码仍有相当难度，该领域仍存在许多问题亟待解决。笔者对分形图像压缩的理论基础、自动分形图像压缩的实现以及分形图像序列压缩等进行了全面的综述，介绍了各种具有代表性的改进算法，阐明了各个算法的原理和特点，最后对目前研究中存在的问题及可能的对策和研究方向进行了讨论。

Avaliação de imagens através de Similaridade Estrutural e do conceito de Mínima Diferença de Cor Perceptível.

A avaliação objetiva da qualidade de imagens é de especial importância em diversas aplicações, por exemplo na compressão de imagens, onde pode ser utilizada para regular a taxa que deve ser empregada para que haja a máxima compressão (permitindo perda de dados) sem comprometer a qualidade final; outro exemplo é na inserção de marcas dágua, isto é, introdução de informações descritivas utilizadas para atestar a autenticidade de uma imagem, que devem ser invisíveis para o observador. O SSIM (Structural SIMilarity) é uma métrica de avaliação objetiva da qualidade de imagens de referência completa projetada para imagens em tons de cinza. Esta dissertação investiga sua aplicação na avaliação de imagens coloridas. Para tanto, inicialmente é feito um estudo do SSIM utilizando quatro diferentes espaços de cores RGB, YCbCr, Lαβ e CIELAB. O SSIM é primeiramente calculado nos canais individuais desses espaços de cores. Em seguida, com inspiração no trabalho desenvolvido em (1) são testadas formas de se combinar os valores SSIM obtidos para cada canal em um valor único os chamados SSIM Compostos. Finalmente, a fim de buscar melhores correlações entre SSIM e avaliação subjetiva, propomos a utilização da mínima diferença de cor perceptível, calculada utilizando o espaço de cores CIELAB, conjuntamente com o SSIM. Para os testes são utilizados três bancos de dados de imagens coloridas, LIVE, IVC e TID, a fim de se conferir consistência aos resultados. A avaliação dos resultados é feita utilizando as métricas empregadas pelo VQEG (Video Quality Experts Group) para a avaliação da qualidade de vídeos, com uma adaptação. As conclusões do trabalho sugerem que os melhores resultados para avaliação da qualidade de imagens coloridas usando o SSIM são obtidas usando os canais de luminância dos espaços de cores YCbCr, Lαβ e especialmente o CIELAB. Também se concluiu que a utilização da mínima diferença de cor perceptível contribui para o melhoramento dos resultados da avaliação objetiva.

基于小波理论的数字图像压缩方法研究

图像压缩是图像处理领域的一个重要研究课题，在图像存储、传输等应用中发挥着至关重要的作用。小波变换具有多分辨率、时频局部性、能量集中能力强等众多优点，因此特别适合于图像压缩。图像压缩主要涉及到图像变换和编码两部分。本文的大部分工作是基于图像变换而展开的，在小波变换理论、基于小波的图像压缩算法理论及其实现、提升小波变换的FPGA实现、以及对两种小波变换算法的改进等方面进行了深入研究和探讨，具体研究内容如下： 1介绍论文的研究背景和意义，并简要介绍图像压缩和小波变换的基本知识和研究现状，概述本论文的主要研究工作。 2详细阐述图像压缩的基本知识，包括图像压缩的原理、分类和图像压缩的国际标准，回顾图像压缩技术的研究历史，并展望一些新的发展方向。 3概述小波变换的基本理论，分析提升小波相对于传统小波变换的优势。分析小波变换应用于图像压缩中的优点，阐述基于小波的分层树集划分（SPIHT）编码算法，并给出实验结果。 4 本文设计了一种并行的二维提升小波变换电路硬件结构，在设计中主要针对小波基的选取、边界拓展方式、提升小波变换数字电路的流水线设计、变换整体结构设计等进行了详细的讨论。这些设计显著降低了资源的消耗，加快了变换速度，提高硬件利用率。 5 由于小波变换缺乏方向性，本文设计了基于Contourlet变换的改进策略，充分利用了Contourlet的方向性，并且克服了其冗余性，使其性能达到最优；针对提升小波不具备平移不变性的特点，将冗余分解的策略引入到提升小波，设计了基于最小熵原则的最佳冗余提升小波分解。并将这两种方法应用图像压缩，取得了较好的效果。

基于SVD和能量最小原则的图像自适应降噪算法

基于奇异值分解和能量最小原则,提出了一种自适应图像降噪算法,并给出了基于有界变差的能量降噪模型的代数形式。通过在矩阵范数意义下求能量最小,自适应确定去噪图像重构的奇异值个数。该算法的特点是将能量最小法则和奇异值分解结合起来,在代数空间中建立了一种自适应的图像降噪算法。与基于压缩比和奇异值分解的降噪方法相比,由于该算法避免了图像压缩比函数及其拐点的计算,因此具有快速去噪和简单可行的优点。实验结果证明,该算法是有效的。

JPEG压缩编码的扩充自适应量化器设计

在ＪＰＥＧ静止图象压缩的基础上，设计了一种扩充的自适应量化器．利用人眼的视觉特征，通过分析ＭＣＵ块的局部视觉活动性，以ＭＣＵ活动性函数确定量化因子，并引入亮度掩盖算子调节量化参量．实验结果表明，本文所设计的自适应量化器能减少图象编码主观失真，改善图象质量，获得更好的压缩效果

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.

Development of a New Transform:MRT

Fourier transform methods are employed heavily in digital signal processing. Discrete Fourier Transform (DFT) is among the most commonly used digital signal transforms. The exponential kernel of the DFT has the properties of symmetry and periodicity. Fast Fourier Transform (FFT) methods for fast DFT computation exploit these kernel properties in different ways. In this thesis, an approach of grouping data on the basis of the corresponding phase of the exponential kernel of the DFT is exploited to introduce a new digital signal transform, named the M-dimensional Real Transform (MRT), for l-D and 2-D signals. The new transform is developed using number theoretic principles as regards its specific features. A few properties of the transform are explored, and an inverse transform presented. A fundamental assumption is that the size of the input signal be even. The transform computation involves only real additions. The MRT is an integer-to-integer transform. There are two kinds of redundancy, complete redundancy & derived redundancy, in MRT. Redundancy is analyzed and removed to arrive at a more compact version called the Unique MRT (UMRT). l-D UMRT is a non-expansive transform for all signal sizes, while the 2-D UMRT is non-expansive for signal sizes that are powers of 2. The 2-D UMRT is applied in image processing applications like image compression and orientation analysis. The MRT & UMRT, being general transforms, will find potential applications in various fields of signal and image processing.

Model-Based Matching by Linear Combinations of Prototypes

We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.

Sparse Correlation Kernel Analysis and Reconstruction

This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images.