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

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

Implementation of convolutional neural networks in accelerating units for real-time image analysis

Matomojo šviesos spektro vaizdų analizė įgalina intelektualiąsias sistemas gauti informaciją taip, kaip žmogus rega. Daugelyje sričių taikoma sąsūkos dirbtinių neuronų tinklais (SDNT) grįsta vaizdų analizė. Tačiau dėl didelės skaičiavimų apimties kyla sunkumų įgyvendinant įterptinėse sistemose lokaliai vykdomus SDNT grįstus algoritmus vaizdų analizei realiuoju laiku. Šiuo metu vaizdų analizei įterptinėse sistemose galima taikyti spartinančiuosius įrenginius, tačiau trūksta suderinamų SDNT elementų sąrašų ir SDNT pritaikymo spartinantiesiems įrenginiams įterptinėse sistemose metodikos. Darbo tyrimų objektas – sąsūkos dirbtinių neuronų tinklai vaizdams analizuoti realiuoju laiku. Disertacijoje tiriami šie, su tiriamuoju objektų susiję dalykai: įgyvendinimas spartinančiuosiuose įrenginiuose ir projektavimo metodika. Disertacijoje pateikiami vaizdams apdoroti skirtų SDNT įgyvendinimo tyrimai, kuriais remiantis sudarytas SDNT elementų sąrašas ir metodika, skirta SDNT pritaikymui įgyvendinti spartinančiuosiuose įrenginiuose pasirinktiems vaizdų analizės uždaviniams spręsti. Taip pat pateikiami dviejų taikymo sričių vaizdų analizės algoritmų, sukurtų taikant pasiūlytą elementų sąrašą ir metodiką, aprašymai. Viena iš sričių – žmogaus akies tinklainės vaizdų požymių aprašymas. Kita sritis – kelio dangos tipo ir būklės įvertinimas, analizuojant vaizdus iš automobilio priekyje sumontuotos vaizdo kameros. Pagrindiniai disertacijos rezultatai yra paskelbti septyniuose moksliniuose straipsniuose recenzuojamuose mokslo leidiniuose: vienas straipsnis mokslo žurnale, referuojamame Clarivate Analytics Web of Science duomenų bazėje, turintis citavimo indeksą 1,524, vienas straipsnis mokslo žurnale, referuojamame Clarivate Analytics Web of Science duomenų bazėje, vienas straipsnis mokslo žurnale, referuojamame Index Copernicus duomenų bazėje, trys straipsniai tarptautinių konferencijų medžiagose, referuojamose Clarivate Analytics Web of Science Proceedings duomenų bazėje, vienas straipsnis konferencijos medžiagoje, referuojamoje kitose duomenų bazėse. Disertacijoje atliktų tyrimų rezultatai buvo pristatyti devyniose mokslinėse konferencijose Lietuvoje ir užsienyje. Disertaciją sudaro įžanga, trys skyriai, bendrosios išvados, literatūros šaltinių sąrašas ir trys priedai.Disertacij

Biosignalų požymių regos diskomfortui vertinti išskyrimas ir tyrimas

Comfortable stereoscopic perception continues to be an essential area of research. The growing interest in virtual reality content and increasing market for head-mounted displays (HMDs) still cause issues of balancing depth perception and comfortable viewing. Stereoscopic views are stimulating binocular cues – one type of several available human visual depth cues which becomes conflicting cues when stereoscopic displays are used. Depth perception by binocular cues is based on matching of image features from one retina with corresponding features from the second retina. It is known that our eyes can tolerate small amounts of retinal defocus, which is also known as Depth of Focus. When magnitudes are larger, a problem of visual discomfort arises. The research object of the doctoral dissertation is a visual discomfort level. This work aimed at the objective evaluation of visual discomfort, based on physiological signals. Different levels of disparity and the number of details in stereoscopic views in some cases make it difficult to find the focus point for comfortable depth perception quickly. During this investigation, a tendency for differences in single sensor-based electroencephalographic EEG signal activity at specific frequencies was found. Additionally, changes in eye tracker collected gaze signals were also found. A dataset of EEG and gaze signal records from 28 control subjects was collected and used for further evaluation. The dissertation consists of an introduction, three chapters and general conclusions. The first chapter reveals the fundamental knowledge ways of measuring visual discomfort based on objective and subjective methods. In the second chapter theoretical research results are presented. This research was aimed to investigate methods which use physiological signals to detect changes on the level of sense of presence. Results of the experimental research are presented in the third chapter. This research aimed to find differences in collected physiological signals when a level of visual discomfort changes. An experiment with 28 control subjects was conducted to collect these signals. The results of the thesis were published in six scientific publications – three in peer-reviewed scientific papers, three in conference proceedings. Additionally, the results of the research were presented in 8 conferences.Dissertatio

Identification and characterisation of rare CACNG5 genetic variants in bipolar disorder and schizophrenia

Schizophrenia (SCZ) and bipolar disorder (BPD) are common, highly heritability psychiatric disorders. Genome-wide association studies have found evidence of shared genetic susceptibility to both diseases. The most notable example is CACNA1C which encodes the 1 subunit of L-type calcium channels. Several other calcium channel genes have also been implicated in BPD and/or SCZ and together there is support for a role for these genes in both diseases. The primary function of several subunit calcium channel genes appears to be the regulation of AMPA receptor localisation and function. Collectively these are known as Transmembrane AMPA receptor Regulatory Proteins (TARPs). This thesis aimed to identify disease relevant genetic variation in one such TARP, CACNG5, and to study the effect of these variants. CACNG5 variants in the exons and promoter region were identified in 1098 BPD, 618 SCZ, and 1087 control individuals. Four novel non-synonymous SNPs (nsSNPs) and four nsSNPs were identified. Burden analysis of nsSNPs in BPD and SCZ found evidence for association (p=0.0022). This association was strengthened by inclusion of data from European samples in the 1000 Genomes project (p=0.00057). However, combined data with the UK10K and Swedish exome sequence studies founds a weakened association signal (p=0.0082). Functional analyses using co-expression of AMPAR2 and CACNG5 constructs containing the eight nsSNPs were used to analyse changes in the expression and/or trafficking of 5 and AMPA receptors. Four of the variants were associated with decreased AMPAR2 expression as a consequence of altered trafficking to the cell surface. V146M (identified in 2 SCZ patients) overexpression increased AMPAR2 trafficking to the cell surface (p<0.005); conversely, T164L (identified in one SCZ patient) overexpression decreased the expression of AMPAR2 and its cell surface trafficking (p<0.05). Our results suggest a role for CACNG5 variants in SCZ and/or BPD and that this may be mediated via dysregulation of AMPAR

Overexpression of tumour-associated carbohydrate antigen sialyl-Tn in advanced bladder tumours

Little is known on the expression of the tumour-associated carbohydrate antigen sialyl-Tn (STn), in bladder cancer. We report here that 75% of the high-grade bladder tumours, presenting elevated proliferation rates and high risk of recurrence/progression expressed STn. However, it was mainly found in non-proliferative areas of the tumour, namely in cells invading the basal and muscle layers. STn was also found in tumour-adjacent mucosa, which suggests its dependence on a field effect of the tumour. Furthermore, it was not expressed by the normal urothelium, demonstrating the cancer-specific nature of this antigen. STn expression correlated with that of sialyltransferase ST6GalNAc.I, its major biosynthetic enzyme. The stable expression of ST6GalNAc.I in the bladder cancer cell line MCR induced STn expression and a concomitant increase of cell motility and invasive capability. Altogether, these results indicate for the first time a link between STn expression and malignancy in bladder cancer. Hence, therapies targeting STn may constitute new treatment approaches for these tumours