JPEG XR scalable coding for remote image browsing applications

The growing popularity of the Internet has opened the road to multimedia and interactivity, emphasizing the importance of visual communication. In this context, digital images have taken a lead role and have an increasing number of applications. Consider, for example, the spread that digital cameras and mobile devices such as mobile phones have become in recent years. Thus, it arises the need for a flexible system that can handle images from different sources and are able to adapt to a different view. The importance of this issue lies in the application scenario: today there are datastores with a large number of images saved in JPEG format and systems for rendering digital images are various and with very different characteristics with each other. The ISO/IEC committee has recently issued a new format, called JPEG-XR, created explicitly for the modern digital cameras. The new coding algorithm JPEG-XR, can overcome various limitations of the first JPEG algorithm and provides viable alternatives to the JPEG2000 algorithm. This research has primarily focused on issues concerning the scalability of the new format of digital images.Additional scalability levels are fundamental for image browsing applications, because enable the system to ensure a correct and efficient functioning even when there is a sharp increase in the number of resources and users.Scalability is mostly required when dealing with large image database on the Web in order to reduce the transferred data, especially when it comes to large images. The interactive browsing also requires the ability to access to arbitrary parts of the image. The starting point is the use of a client-server architecture, in which the server stores a database of JPEG XR images and analyzes requests from a client. Client and server communicate via HTTP and use an exchange protocol. In order to minimize the transferred information, the JPEG XR coded file format should make use of the frequency mode order and partitioning of images into optimized tiles. The main goal is transmitting only some subset of the available sub-band coefficients. This is necessary to allow access an interactive access to portion of images, that are downloaded and displayed, minimizing the amount of data transferred and maintaining an acceptable image quality.The proposed architecture has of course prompted a study of errors in transmission on unreliable channel, such as the wireless one, and the definition of possible optimizations/variants of the codec in order to overcome its own limitations. Image data compressed with JPEG XR when transmitted over error-prone channels is severely distorted. In fact, due to the adaptive coding strategies used by the codec, even a single bit error causes a mismatch in the alignment of the reading position from the bit-stream, leading to completely different images at the decoder side. An extension to the JPEG XR algorithm is proposed, consisting in an error recovery process enabling the decoder to realign itself to the right bit-stream position and to correctly decode the most part of the image. Several experiments have been performed using different encoder parameter and different error probabilities while image distortion is measured by PSNR objective metric. The simplicity of the proposed algorithm adds very little computational overhead and seems very promising as confirmed by objective image quality results in experimental tests

Investigation of Sparsifying Transforms in Compressed Sensing for Magnetic Resonance Imaging with Fasttestcs

The goal of this contribution is to achieve higher reduction factors for faster Magnetic Resonance Imaging (MRI) scans with better Image Quality (IQ) by using Compressed Sensing (CS). This can be accomplished by adopting and understanding better sparsifying transforms for CS in MRI. There is a tremendous number of transforms and optional settings potentially available. Additionally, the amount of research in CS is growing, with possible duplication and difficult practical evaluation and comparison. However, no in-depth analysis of the effectiveness of different redundant sparsifying transforms on MRI images with CS has been undertaken until this work. New theoretical sparsity bounds for the dictionary restricted isometry property constants in CS are presented with mathematical proof. In order to verify the sparsifying transforms in this setting, the experiments focus on several redundant transforms contrasting them with orthogonal transforms. The transforms investigated are Wavelet (WT), Cosine (CT), contourlet, curvelet, k-means singular value decomposition, and Gabor. Several variations of these transforms with corresponding filter options are developed and tested in compression and CS simulations. Translation Invariance (TI) in transforms is found to be a key contributing factor in producing good IQ because any particular translation of the signal will not effect the transform representation. Some transforms tested here are TI and many others are made TI by transforming small overlapping image patches. These transforms are tested by comparing different under-sampling patterns and reduction ratios with varying image types including MRI data. Radial, spiral, and various random patterns are implemented and demonstrate that the TIWT is very robust across all under-sampling patterns. Results of the TIWT simulations show improvements in de-noising and artifact suppression over that of individual orthogonal wavelets and total variation ell-1 minimization in CS simulations. Some of these transforms add considerable time to the CS simulations and prohibit extensive testing of large 3D MRI datasets. Therefore, the FastTestCS software simulation framework is developed and customized for testing images, under-samping patterns and sparsifying transforms. This novel software is offered as a practical, robust, universal framework for evaluating and developing simulations in order to quickly test sparsifying transforms for CS MRI

Motion compensation and very low bit rate video coding

Recently, many activities of the International Telecommunication Union (ITU) and the International Standard Organization (ISO) are leading to define new standards for very low bit-rate video coding, such as H.263 and MPEG-4 after successful applications of the international standards H.261 and MPEG-1/2 for video coding above 64kbps. However, at very low bit-rate the classic block matching based DCT video coding scheme suffers seriously from blocking artifacts which degrade the quality of reconstructed video frames considerably. To solve this problem, a new technique in which motion compensation is based on dense motion field is presented in this dissertation. Four efficient new video coding algorithms based on this new technique for very low bit-rate are proposed. (1) After studying model-based video coding algorithms, we propose an optical flow based video coding algorithm with thresh-olding techniques. A statistic model is established for distribution of intensity difference between two successive frames, and four thresholds are used to control the bit-rate and the quality of reconstructed frames. It outperforms the typical model-based techniques in terms of complexity and quality of reconstructed frames. (2) An efficient algorithm using DCT coded optical flow. It is found that dense motion fields can be modeled as the first order auto-regressive model, and efficiently compressed with DCT technique, hence achieving very low bit-rate and higher visual quality than the H.263/TMN5. (3) A region-based discrete wavelet transform video coding algorithm. This algorithm implements dense motion field and regions are segmented according to their content significance. The DWT is applied to residual images region by region, and bits are adaptively allocated to regions. It improves the visual quality and PSNR of significant regions while maintaining low bit-rate. (4) A segmentation-based video coding algorithm for stereo sequence. A correlation-feedback algorithm with Kalman filter is utilized to improve the accuracy of optical flow fields. Three criteria, which are associated with 3-D information, 2-D connectivity and motion vector fields, respectively, are defined for object segmentation. A chain code is utilized to code the shapes of the segmented objects. it can achieve very high compression ratio up to several thousands

Digital Holography Data Compression

Digital holography processing is a research topic related to the development of novel visual immersive applications. The huge amount of information conveyed by a digital hologram and the different properties of holographic data with respect to conventional photographic data require a comprehension of the performances and limitations of current image and video standard techniques. This paper proposes an architecture for objective evaluation of the performances of the state-of-the-art compression techniques applied to digital holographic data

Structured random measurements in signal processing

Compressed sensing and its extensions have recently triggered interest in randomized signal acquisition. A key finding is that random measurements provide sparse signal reconstruction guarantees for efficient and stable algorithms with a minimal number of samples. While this was first shown for (unstructured) Gaussian random measurement matrices, applications require certain structure of the measurements leading to structured random measurement matrices. Near optimal recovery guarantees for such structured measurements have been developed over the past years in a variety of contexts. This article surveys the theory in three scenarios: compressed sensing (sparse recovery), low rank matrix recovery, and phaseless estimation. The random measurement matrices to be considered include random partial Fourier matrices, partial random circulant matrices (subsampled convolutions), matrix completion, and phase estimation from magnitudes of Fourier type measurements. The article concludes with a brief discussion of the mathematical techniques for the analysis of such structured random measurements.Comment: 22 pages, 2 figure

Selected topics on distributed video coding

Distributed Video Coding (DVC) is a new paradigm for video compression based on the information theoretical results of Slepian and Wolf (SW), and Wyner and Ziv (WZ). While conventional coding has a rigid complexity allocation as most of the complex tasks are performed at the encoder side, DVC enables a flexible complexity allocation between the encoder and the decoder. The most novel and interesting case is low complexity encoding and complex decoding, which is the opposite of conventional coding. While the latter is suitable for applications where the cost of the decoder is more critical than the encoder's one, DVC opens the door for a new range of applications where low complexity encoding is required and the decoder's complexity is not critical. This is interesting with the deployment of small and battery-powered multimedia mobile devices all around in our daily life. Further, since DVC operates as a reversed-complexity scheme when compared to conventional coding, DVC also enables the interesting scenario of low complexity encoding and decoding between two ends by transcoding between DVC and conventional coding. More specifically, low complexity encoding is possible by DVC at one end. Then, the resulting stream is decoded and conventionally re-encoded to enable low complexity decoding at the other end. Multiview video is attractive for a wide range of applications such as free viewpoint television, which is a system that allows viewing the scene from a viewpoint chosen by the viewer. Moreover, multiview can be beneficial for monitoring purposes in video surveillance. The increased use of multiview video systems is mainly due to the improvements in video technology and the reduced cost of cameras. While a multiview conventional codec will try to exploit the correlation among the different cameras at the encoder side, DVC allows for separate encoding of correlated video sources. Therefore, DVC requires no communication between the cameras in a multiview scenario. This is an advantage since communication is time consuming (i.e. more delay) and requires complex networking. Another appealing feature of DVC is the fact that it is based on a statistical framework. Moreover, DVC behaves as a natural joint source-channel coding solution. This results in an improved error resilience performance when compared to conventional coding. Further, DVC-based scalable codecs do not require a deterministic knowledge of the lower layers. In other words, the enhancement layers are completely independent from the base layer codec. This is called the codec-independent scalability feature, which offers a high flexibility in the way the various layers are distributed in a network. This thesis addresses the following topics: First, the theoretical foundations of DVC as well as the practical DVC scheme used in this research are presented. The potential applications for DVC are also outlined. DVC-based schemes use conventional coding to compress parts of the data, while the rest is compressed in a distributed fashion. Thus, different conventional codecs are studied in this research as they are compared in terms of compression efficiency for a rich set of sequences. This includes fine tuning the compression parameters such that the best performance is achieved for each codec. Further, DVC tools for improved Side Information (SI) and Error Concealment (EC) are introduced for monoview DVC using a partially decoded frame. The improved SI results in a significant gain in reconstruction quality for video with high activity and motion. This is done by re-estimating the erroneous motion vectors using the partially decoded frame to improve the SI quality. The latter is then used to enhance the reconstruction of the finally decoded frame. Further, the introduced spatio-temporal EC improves the quality of decoded video in the case of erroneously received packets, outperforming both spatial and temporal EC. Moreover, it also outperforms error-concealed conventional coding in different modes. Then, multiview DVC is studied in terms of SI generation, which differentiates it from the monoview case. More specifically, different multiview prediction techniques for SI generation are described and compared in terms of prediction quality, complexity and compression efficiency. Further, a technique for iterative multiview SI is introduced, where the final SI is used in an enhanced reconstruction process. The iterative SI outperforms the other SI generation techniques, especially for high motion video content. Finally, fusion techniques of temporal and inter-view side informations are introduced as well, which improves the performance of multiview DVC over monoview coding. DVC is also used to enable scalability for image and video coding. Since DVC is based on a statistical framework, the base and enhancement layers are completely independent, which is an interesting property called codec-independent scalability. Moreover, the introduced DVC scalable schemes show a good robustness to errors as the quality of decoded video steadily decreases with error rate increase. On the other hand, conventional coding exhibits a cliff effect as the performance drops dramatically after a certain error rate value. Further, the issue of privacy protection is addressed for DVC by transform domain scrambling, which is used to alter regions of interest in video such that the scene is still understood and privacy is preserved as well. The proposed scrambling techniques are shown to provide a good level of security without impairing the performance of the DVC scheme when compared to the one without scrambling. This is particularly attractive for video surveillance scenarios, which is one of the most promising applications for DVC. Finally, a practical DVC demonstrator built during this research is described, where the main requirements as well as the observed limitations are presented. Furthermore, it is defined in a setup being as close as possible to a complete real application scenario. This shows that it is actually possible to implement a complete end-to-end practical DVC system relying only on realistic assumptions. Even though DVC is inferior in terms of compression efficiency to the state of the art conventional coding for the moment, strengths of DVC reside in its good error resilience properties and the codec-independent scalability feature. Therefore, DVC offers promising possibilities for video compression with transmission over error-prone environments requirement as it significantly outperforms conventional coding in this case

Fast Image Restoration Method Based on the Multi-Resolution Layer

[[abstract]]When transmitted through a poor quality network or stored on an unstable storage media, block-based code images will experience the block loss. To restore damaged images suffering from block loss, Best Neighborhood Matching and Jump and Look-Around BNM provide the most effective image restoration. However, while BNM offers good restoration quality, it requires a large calculation time. By “JUMP” method, JLBNM can effectively shorten the computation time but this comes at the cost of a loss in quality. We have therefore proposed a new image inpainting technique that uses theWavelet Domain to deliver fast computation time and high restoration quality Wavelet Stage BNM. Our proposed reconstruction algorithm includes three optimization techniques change of analytical domain, consideration of texture composition and a new decision-making mechanism: DirectionalWaveletWeighted Method. Theoretical analysis and experimental results demonstrate our method delivered fast computation time and high restoration quality.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙