SP-DSTS-MIMO scheme-aided H.266 for reliable high data rate mobile video communication

With the ever growth of Internet users, video applications, and massive data traffic across the network, there is a higher need for reliable bandwidth-efficient multimedia communication. Versatile Video Coding (VVC/H.266) is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding (HEVC) while providing versatile effective use for Ultra-High Definition (HD) videos. This article analyzes the quality performance of convolutional codes, turbo codes and self-concatenated convolutional (SCC) codes based on performance metrics for reliable future video communication. The advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon limit. Turbo codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional (RSC) encoders in a parallel fashion. Constituent RSC encoders may be operating on the same or different architectures and code rates. The proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading (SP-DSTS) for video transmission in order to provide bandwidth-efficient wireless video communication. Moreover, simulation results show that turbo codes defeat convolutional codes with an averaged E-b/N-0 gain of 1.5 dB while convolutional codes outperform compared to SCC codes with an E-b/N-0 gain of 3.5 dB at Bit Error Rate (BER) of 10(-4). The Peak Signal to Noise Ratio (PSNR) results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E-b/N-0 value of 4.5 dB.Web of Science741101099

From Error Probability to Information Theoretic (Multi-Modal) Signal Processing

We propose an information theoretic model that unifies a wide range of existing information theoretic signal processing algorithms in a compact mathematical framework. It is mainly based on stochastic processes, Markov chains and error probabilities. The proposed framework will allow us to discuss revealing analogies and differences between several well known algorithms and to propose interesting extensions resulting directly from our formalism. We will then describe how the theory can be applied to the rapidly emerging field of multi-modal signal processing: we will show how our framework can be efficiently used for multi-modal medical image processing and for joint analysis of multi-media sequences (audio and video)

Graph-based Estimation of Information Divergence Functions

abstract: Information divergence functions, such as the Kullback-Leibler divergence or the Hellinger distance, play a critical role in statistical signal processing and information theory; however estimating them can be challenge. Most often, parametric assumptions are made about the two distributions to estimate the divergence of interest. In cases where no parametric model fits the data, non-parametric density estimation is used. In statistical signal processing applications, Gaussianity is usually assumed since closed-form expressions for common divergence measures have been derived for this family of distributions. Parametric assumptions are preferred when it is known that the data follows the model, however this is rarely the case in real-word scenarios. Non-parametric density estimators are characterized by a very large number of parameters that have to be tuned with costly cross-validation. In this dissertation we focus on a specific family of non-parametric estimators, called direct estimators, that bypass density estimation completely and directly estimate the quantity of interest from the data. We introduce a new divergence measure, the $D_p$-divergence, that can be estimated directly from samples without parametric assumptions on the distribution. We show that the $D_p$-divergence bounds the binary, cross-domain, and multi-class Bayes error rates and, in certain cases, provides provably tighter bounds than the Hellinger divergence. In addition, we also propose a new methodology that allows the experimenter to construct direct estimators for existing divergence measures or to construct new divergence measures with custom properties that are tailored to the application. To examine the practical efficacy of these new methods, we evaluate them in a statistical learning framework on a series of real-world data science problems involving speech-based monitoring of neuro-motor disorders.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Imaging Applications of Stochastic Minimal Graphs

Abstract — This paper presents an overview of some of the recent theory and application of stochastic minimal graphs in the context of entropy estimation for imaging applications. Stochastic graphs which span a set of extracted image features can be constructed to yield consistent estimators of Jensen’s entropy difference for between pairs of images. Unlike traditional plug-in entropy estimates based on density estimation, stochastic graph methods provide direct estimates of these quantities. We review the stochastic graph approach to entropy estimation, compare convergence rates to that of plug-in estimators, and discuss a geo-registration application. An extended version of this paper is the technical report [4]. I

Imaging applications of stochastic minimal graphs

Abstract — This paper presents an overview of some of the recent the- formation method outlined in [6]. However, for unknown� ory and application of stochastic minimal graphs in the context of entropy and unknown�the existence of consistent minimal-graph es-estimation for imaging applications. Stochastic graphs which span a set of extracted image features can be constructed to yield consistent estimatimators of�«���is an open problem. This paper will be tors of Jensen’s entropy difference for between pairs of images. Unlike concerned with an alternative dissimilarity function, called the traditional plug-in entropy estimates based on density estimation, stochas«-Jensen difference, which is a function of the joint entropy of tic graph methods provide direct estimates of these quantities. We review�and�.As will be shown below, this function can be esti-the stochastic graph approach to entropy estimation, compare convergence rates to that of plug-in estimators, and discuss a geo-registration applicamated using minimal graph entropy estimation techniques an