Fully Scalable Video Coding Using Redundant-Wavelet Multihypothesis and Motion-Compensated Temporal Filtering

In this dissertation, a fully scalable video coding system is proposed. This system achieves full temporal, resolution, and fidelity scalability by combining mesh-based motion-compensated temporal filtering, multihypothesis motion compensation, and an embedded 3D wavelet-coefficient coder. The first major contribution of this work is the introduction of the redundant-wavelet multihypothesis paradigm into motion-compensated temporal filtering, which is achieved by deploying temporal filtering in the domain of a spatially redundant wavelet transform. A regular triangle mesh is used to track motion between frames, and an affine transform between mesh triangles implements motion compensation within a lifting-based temporal transform. Experimental results reveal that the incorporation of redundant-wavelet multihypothesis into mesh-based motion-compensated temporal filtering significantly improves the rate-distortion performance of the scalable coder. The second major contribution is the introduction of a sliding-window implementation of motion-compensated temporal filtering such that video sequences of arbitrarily length may be temporally filtered using a finite-length frame buffer without suffering from severe degradation at buffer boundaries. Finally, as a third major contribution, a novel 3D coder is designed for the coding of the 3D volume of coefficients resulting from the redundant-wavelet based temporal filtering. This coder employs an explicit estimate of the probability of coefficient significance to drive a nonadaptive arithmetic coder, resulting in a simple software implementation. Additionally, the coder offers the possibility of a high degree of vectorization particularly well suited to the data-parallel capabilities of modern general-purpose processors or customized hardware. Results show that the proposed coder yields nearly the same rate-distortion performance as a more complicated coefficient coder considered to be state of the art

An Optimal HSI Image Compression using DWT and CP

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. An efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and CANDECOM/PARAFAC (DWT-CP), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply CP on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and CP to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance when comparing with the existing method PCA with JPEG 2000 and 3D SPECK.DOI:http://dx.doi.org/10.11591/ijece.v4i3.6326

Wavelet Based Color Image Compression and Mathematical Analysis of Sign Entropy Coding

International audienceOne of the advantages of the Discrete Wavelet Transform (DWT) compared to Fourier Transform (e.g. Discrete Cosine Transform DCT) is its ability to provide both spatial and frequency localization of image energy. However, WT coefficients, like DCT coefficients, are defined by magnitude as well as sign. While algorithms exist for the coding of wavelet coefficients magnitude, there are no efficient for coding their sign. In this paper, we propose a new method based on separate entropy coding of sign and magnitude of wavelet coefficients. The proposed method is applied to the standard color test images Lena, Peppers, and Mandrill. We have shown that sign information of wavelet coefficients as well for the luminance as for the chrominance, and the refinement information of the quantized wavelet coefficients may not be encoded by an estimated probability of 0.5. The proposed method is evaluated; the results obtained are compared to JPEG2000 and SPIHT codec. We have shown that the proposed method has significantly outperformed the JPEG2000 and SPIHT codec as well in terms of PSNR as in subjective quality. We have proved, by an original mathematical analysis of the entropy, that the proposed method uses a minimum bit allocation in the sign information coding

A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders

[EN] Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this paper, we analyze the convenience of including sign-coding techniques into wavelet-based image encoders and propose a methodology that allows the design of sign-prediction tools for whatever kind of wavelet-based encoder. The proposed methodology is based on the use of metaheuristic algorithms to find the best sign prediction with the most appropriate context distribution that maximizes the resulting sign-compression rate of a particular wavelet encoder. Following our proposal, we have designed and implemented a sign-coding module for the LTW wavelet encoder, to evaluate the benefits of the sign-coding tool provided by our proposed methodology. The experimental results show that sign compression can save up to 18.91% of bit-rate when enabling sign-coding capabilities. Also, we have observed two general behaviors when coding the sign of wavelet coefficients: (a) the best results are provided from moderate to high compression rates; and (b) the sign redundancy may be better exploited when working with high-textured images.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant RTI2018-098156-B-C54, co-financed by FEDER funds (MINECO/FEDER/UE).López-Granado, OM.; Martínez-Rach, MO.; Martí-Campoy, A.; Cruz-Chávez, MA.; Pérez Malumbres, M. (2020). A General Model for the Design of Efficient Sign-Coding Tools for Wavelet-Based Encoders. Electronics. 9(11):1-17. https://doi.org/10.3390/electronics9111899S117911Said, A., & Pearlman, W. A. (1996). A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology, 6(3), 243-250. doi:10.1109/76.499834ISO/IEC 15444-1:2019. Information technology—JPEG 2000 Image Coding System—Part 1: Core Coding Systemhttps://www.iso.org/standard/78321.htmlTaubman, D. (2000). High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 9(7), 1158-1170. doi:10.1109/83.847830Bilgin, A., Sementilli, P. J., & Marcellin, M. W. (1999). Progressive image coding using trellis coded quantization. IEEE Transactions on Image Processing, 8(11), 1638-1643. doi:10.1109/83.799891Oliver, J., & Malumbres, M. P. (2006). Low-Complexity Multiresolution Image Compression Using Wavelet Lower Trees. IEEE Transactions on Circuits and Systems for Video Technology, 16(11), 1437-1444. doi:10.1109/tcsvt.2006.883505Cho, Y., & Pearlman, W. A. (2007). Hierarchical Dynamic Range Coding of Wavelet Subbands for Fast and Efficient Image Decompression. IEEE Transactions on Image Processing, 16(8), 2005-2015. doi:10.1109/tip.2007.901247Deever, A. T., & Hemami, S. S. (2003). Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs. IEEE Transactions on Image Processing, 12(4), 420-430. doi:10.1109/tip.2003.811499Mallat, S., & Zhong, S. (1992). Characterization of signals from multiscale edges. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(7), 710-732. doi:10.1109/34.142909López-Granado, O., Galiano, V., Martí, A., Migallón, H., Martínez-Rach, M., Piñol, P., & Malumbres, M. P. (2013). Improving image compression through the use of evolutionary computing algorithms. Data Management and Security. doi:10.2495/data130041Kodak Lossless True Color Image Suitehttp://r0k.us/graphics/kodak/Rawzor—Lossless Compression Software for Camera Raw Imageshttp://imagecompression.info/test_images

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

The What-And-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding

The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where fllter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simultaneously represent the position, orientation, ancl size of all tbe figures in a scene (where they are). This spatial map may he used to direct spatially localized attention to these image features. A multiscale array of oriented detectors, followed by competitve and interpolative interactions between position, orientation, and size scales, is used to define the Where filter. This analysis discloses several issues that need to be dealt with by a spatial mapping system that is based upon oriented filters, such as the role of cliff filters with and without normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties across orientation than across size scale. Several computationally efficient Where filters are proposed. The Where filter rnay be used for parallel transformation of multiple image figures into invariant representations that are insensitive to the figures' original position, orientation, and size. These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can he used to rapidly search in parallel for a desired target in a scene. Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding. The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentive spatial localization and orientation, whereas a What processing stream is used for attentive object learning and recognition.Advanced Research Projects Agency (ONR-N00014-92-J-4015, AFOSR 90-0083); British Petroleum (89-A-1204); National Science Foundation (IRI-90-00530, Graduate Fellowship); Office of Naval Research (N00014-91-J-4100, N00014-95-1-0409, N00014-95-1-0657); Air Force Office of Scientific Research (F49620-92-J-0499, F49620-92-J-0334

Quantifying the Relationship Among Ground Penetrating Radar Reflection Amplitudes, Horizontal Sub-Wavelength Bedrock Fracture Geometries, and Fluid Conductivities

Accurate characterization of subsurface fractures is indispensible for contaminant transport and fresh water resource modeling because discharge is cubically related to the fracture aperture; thus, minor errors in aperture estimates may yield major errors in a modeled hydrologic response. Ground penetrating radar (GPR) has been successfully used to noninvasively estimate fracture aperture for sub-horizontal fractures at outcrop scale, but limits on vertical and horizontal resolution are a concern. Theoretical formulations and field tests have demonstrated increased GPR amplitude response with the addition of a saline tracer in a sub-millimeter fracture; however, robust verification of existing theoretical equations without an accurate measure of aperture variation across a fracture surface is difficult. The work presented here is directed at better verification of theoretical predictions of GPR amplitude and phase response. For sub-vertical resolution features, the response of a 1000 MHz PulseEKKO Pro transducer to a fluid-filled bedrock fracture analog composed of two plastic (UHMW-PE) blocks was measured, where fracture aperture ranged from 0-40 ± 0.3 mm and fluid conductivity from 0-5700 ± 5 mS/m. The GPR profiles were acquired down the centerline of the block, horizontally stacked to reduce errors, normalized to the control response at zero aperture, used to calculate reflection coefficient by dividing by the magnitude of the direct wave, and used to calculate the instantaneous phase. For sub-horizontal resolution features, lateral fracture extent ranged from 0-20 cm and fluid conductivity from 20-5700 ± 5 mS/m. GPR profiles were acquired parallel and perpendicular to the fracture. Comparison of the measured GPR response to analytical and numerical modeling suggests that numerical modeling best predicts both amplitude and phase variations due to changes in fracture aperture and conductivity. The Widess equation combined with an empirically derived scaling factor also predicts GPR amplitude response but not phase. Future applications to inversions of field data to map subsurface fracture networks will rely on easily invertible models, and numerical modeling using GPRMax2D can help develop a theoretical model for computationally effective and accurate inversion

Computational aspects of parvalbumin-positive interneuron function

The activity of neurons is dependent on the manner in which they process synaptic inputs from other cells. In the event of clustered synaptic input, neurons can respond in a nonlinear manner through synaptic and dendritic mechanisms. Such mechanisms are well established in principal excitatory neurons throughout the brain, where they increase neuronal computational ability and information storage capacity. In contrast for parvalbumin-positive (PV+) interneurons, the most common cortical class of in- hibitory interneuron, synaptic integration is thought to be either linear or sub-linear in nature, facilitating their role as mediators of precise and fast inhibition. This thesis addresses situations in which PV+ interneurons integrate synaptic inputs in a nonlinear manner, and explores the functions of this synaptic processing. First, I describe a form of cooperative supralinear synaptic integration by local excitatory inputs onto PV+ interneurons, and I extend these results to show how this augments the computational capability of PV+ cells within spiking neuron networks. I also explore the importance of polyamine-modulation of synaptic receptors in mediating sublinear synaptic integration, and discuss how this expands the array of mechanisms known to perform similar functions in PV+ cells. Finally, I present work manipulating PV+ cells experimentally during epilepsy. I consider these findings together with recent scientific advances and suggest how they account for a number of open questions and previously contradictory theories of PV+ interneuron function