Segmentation based coding of depth Information for 3D video

Increased interest in 3D artifact and the need of transmitting, broadcasting and saving the whole information that represents the 3D view, has been a hot topic in recent years. Knowing that adding the depth information to the views will increase the encoding bitrate considerably, we decided to find a new approach to encode/decode the depth information for 3D video. In this project, different approaches to encode/decode the depth information are experienced and a new method is implemented which its result is compared to the best previously developed method considering both bitrate and quality (PSNR)

Video Compression for Camera Networks: A Distributed Approach

The problem of finding efficient communications techniques to distribute multi-view video content across different devices and users in a network is receiving a great attention in the last years. Much interest in particular has been devoted recently to the so called field of Distributed Video Coding (DVC). After briefly reporting traditional approaches to multiview coding, this chapter will introduce the field of DVC for multi-camera systems. The theoretical background of Distributed Source Coding (DSC) is first concisely presented and the problem of the application of DSC principles to the case of video sources is then analyzed. The topic is presented discussing approaches to the problem of DVC in both single-view and in multi-view applications

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

State-of-the-Art and Trends in Scalable Video Compression with Wavelet Based Approaches

3noScalable Video Coding (SVC) differs form traditional single point approaches mainly because it allows to encode in a unique bit stream several working points corresponding to different quality, picture size and frame rate. This work describes the current state-of-the-art in SVC, focusing on wavelet based motion-compensated approaches (WSVC). It reviews individual components that have been designed to address the problem over the years and how such components are typically combined to achieve meaningful WSVC architectures. Coding schemes which mainly differ from the space-time order in which the wavelet transforms operate are here compared, discussing strengths and weaknesses of the resulting implementations. An evaluation of the achievable coding performances is provided considering the reference architectures studied and developed by ISO/MPEG in its exploration on WSVC. The paper also attempts to draw a list of major differences between wavelet based solutions and the SVC standard jointly targeted by ITU and ISO/MPEG. A major emphasis is devoted to a promising WSVC solution, named STP-tool, which presents architectural similarities with respect to the SVC standard. The paper ends drawing some evolution trends for WSVC systems and giving insights on video coding applications which could benefit by a wavelet based approach.partially_openpartially_openADAMI N; SIGNORONI. A; R. LEONARDIAdami, Nicola; Signoroni, Alberto; Leonardi, Riccard

Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p

An Introduction to Neural Data Compression

Neural compression is the application of neural networks and other machine learning methods to data compression. Recent advances in statistical machine learning have opened up new possibilities for data compression, allowing compression algorithms to be learned end-to-end from data using powerful generative models such as normalizing flows, variational autoencoders, diffusion probabilistic models, and generative adversarial networks. The present article aims to introduce this field of research to a broader machine learning audience by reviewing the necessary background in information theory (e.g., entropy coding, rate-distortion theory) and computer vision (e.g., image quality assessment, perceptual metrics), and providing a curated guide through the essential ideas and methods in the literature thus far

Some Intra-Frame and Inter-Frame Processing Schemes for Efficient Video Compression

Rapid increase in digital applications due to recent advances in digital communication and devices needs significant video information storing, processing and transmitting. But the amount of original captured video data is huge and thus makes the system complex in all kind of video processing.But applications demand a faster transmission in different sized electronic devices with good quality.Along with, limited bandwidth and memory for storage makes it challenging. These practical constraints for processing a huge amount of video data, makes video compression as active and challenging field of research. The aim of video compression is to remove redundancy of raw video while maintaining the quality and fidelity. For inter frame processing, motion estimation technique is significantly used to reduce temporal redundancy in almost all the video coding standards e.g. MPEG2, MPEG4, H264/AVC which uses state-of-art algorithm to provide higher compression with a perceptual quality.Though motion estimation is main contributor for higher compression, this is the most computationally complex part of video coding tools. So, it is always a requirement to design an algorithm that is both faster and accurate and provides higher compression but good quality output. The goal of this project is to propose an algorithm for motion estimation which will meet all the requirements and overcome all the practical limitations. In this thesis we analyze the motion of video sequences and some novel block matching based motion estimation algorithms are proposed to improve video coding efficiency in inter frame processing. Particle Swarm Optimization technique and Differential Evolutionary model is used for fast and accurate motion estimation and compensation. Spatial and temporal correlation is adapted for initial population. We followed some strategy for adaptive generations, particle population, particle location history preservation and exploitation. The experimental result shows that our proposed algorithm is efficient to maintain the accuracy. There is significant reduction of search points and thus computational complexity while achieving comparable performance in video coding. Spatial domain redundancy is reduced skipping the irrelevant or spatially co-related data by different sub-sampling algorithm.The sub-sampled intra-frame is up-sampled at the receiver side. The up-sampled high resolution frame requires to have good quality . The existing up-sampling or interpolation techniques produce undesirable blurring and ringing artifacts. To alleviate this problem, a novel spatio-temporal pre-processing approach is proposed to improve the quality. The proposed method use low frequency DCT (Discrete cosine transform) component to sub-sample the frame at the transmitter side. In transmitter side a preprocessing method is proposed where the received subsampled frame is passed through a Wiener filter which uses its local statistics in 3×3 neighborhood to modify pixel values. The output of Wiener filter is added with optimized multiple of high frequency component. The output is then passed through a DCT block to up-sample. Result shows that the proposed method outperforms popularly used interpolation techniques in terms of quality measure

Motion correlation based low complexity and low power schemes for video codec

制度:新 ; 報告番号:甲3750号 ; 学位の種類:博士(工学) ; 授与年月日:2012/11/19 ; 早大学位記番号:新6121Waseda Universit