63 research outputs found
High Quality Multimedia Streaming Up Sampler for Android Platform MobWS
In modern era internet is fastest mean of digital transportations and use of mobile devices is emerging to access digitized data, multimedia, sports, videos, TV shows, websites etc. from anyplace, anytime. Also people can share live videos mobile to mobile. However, existing methods are having limitations of resources like bandwidth is shared among different clients, which is resulted into drawback of video streaming. Many new mobile devices with high hardware configuration are present in market to support the high resolution by Apple, Sony, Micromax, Google, etc. but because of low resolution in multimedia streaming it will not support to these new mobile devices. This can result into introduction of visual distortion and artefacts. Thus, to provide high quality video streaming and optimized Mobile Web Service (MobWS) with more ease for mobile devices, method is proposed. This investigated approach is to enable the hosting of WebPages with live videos on android smart phones and bridges resolution gap between end user mobile device and multimedia streaming. This up sampling system is designed to evaluate high-quality multimedia streaming onto mobile phones. That is real time video broadcasting and synchronizing to client device with high resolution, to be done with less computation time as compared to previous approaches
A Bayesian Approach to Block Structure Inference in AV1-based Multi-rate Video Encoding
Due to differences in frame structure, existing multi-rate video encoding
algorithms cannot be directly adapted to encoders utilizing special reference
frames such as AV1 without introducing substantial rate-distortion loss. To
tackle this problem, we propose a novel bayesian block structure inference
model inspired by a modification to an HEVC-based algorithm. It estimates the
posterior probabilistic distributions of block partitioning, and adapts early
terminations in the RDO procedure accordingly. Experimental results show that
the proposed method provides flexibility for controlling the tradeoff between
speed and coding efficiency, and can achieve an average time saving of 36.1%
(up to 50.6%) with negligible bitrate cost.Comment: published in IEEE Data Compression Conference, 201
Analysis and resynthesis of polyphonic music
This thesis examines applications of Digital Signal Processing to the analysis, transformation, and resynthesis of musical audio. First I give an overview of the human perception of music. I then examine in detail the requirements for a system that can analyse, transcribe, process, and resynthesise monaural polyphonic music. I then describe and compare the possible hardware and software platforms. After this I describe a prototype hybrid system that attempts to carry out these tasks using a method based on additive synthesis. Next I present results from its application to a variety of musical examples, and critically assess its performance and limitations. I then address these issues in the design of a second system based on Gabor wavelets. I conclude by summarising the research and outlining suggestions for future developments
Learning temporal variations for action recognition
As a core problem in video analysis, action recognition is of great significance for many higher-level tasks, both in research and industrial applications. With more and more video data being produced and shared daily, effective automatic action recognition methods are needed. Although, many deep-learning methods have been proposed to solve the problem, recent research reveals that single-stream, RGB-based networks are always outperformed by two-stream networks using both RGB and optical flow as inputs. This dependence on optical flow, which indicates a deficiency in learning motion, is present not only in 2D networks but also in 3D networks. This is somewhat surprising since 3D networks are explicitly designed for spatio-temporal learning.
In this thesis, we assume that this deficiency is caused by difficulties associated with learning from videos exhibiting strong temporal variations, such as sudden motion, occlusions, acceleration, or deceleration. Temporal variations occur commonly in real-world videos and force a neural network to account for them, but often are not useful for recognizing actions at coarse granularity. We propose a Dynamic Equilibrium Module (DEM) for spatio-temporal learning through adaptive Eulerian motion manipulation. The proposed module can be inserted into existing networks with separate spatial and temporal convolutions, like the R(2+1)D model, to effectively handle temporal video variations and learn more robust spatio-temporal features. We demonstrate performance gains due to the use of DEM in the R(2+1)D model on miniKinetics, UCF-101, and HMDB-51 datasets
Scalable Video Streaming with Prioritised Network Coding on End-System Overlays
PhDDistribution over the internet is destined to become a standard approach for live broadcasting
of TV or events of nation-wide interest. The demand for high-quality live video
with personal requirements is destined to grow exponentially over the next few years. Endsystem
multicast is a desirable option for relieving the content server from bandwidth bottlenecks
and computational load by allowing decentralised allocation of resources to the users
and distributed service management. Network coding provides innovative solutions for a
multitude of issues related to multi-user content distribution, such as the coupon-collection
problem, allocation and scheduling procedure. This thesis tackles the problem of streaming
scalable video on end-system multicast overlays with prioritised push-based streaming.
We analyse the characteristic arising from a random coding process as a linear channel
operator, and present a novel error detection and correction system for error-resilient decoding,
providing one of the first practical frameworks for Joint Source-Channel-Network
coding. Our system outperforms both network error correction and traditional FEC coding
when performed separately. We then present a content distribution system based on endsystem
multicast. Our data exchange protocol makes use of network coding as a way to
collaboratively deliver data to several peers. Prioritised streaming is performed by means
of hierarchical network coding and a dynamic chunk selection for optimised rate allocation
based on goodput statistics at application layer. We prove, by simulated experiments, the
efficient allocation of resources for adaptive video delivery. Finally we describe the implementation
of our coding system. We highlighting the use rateless coding properties, discuss
the application in collaborative and distributed coding systems, and provide an optimised
implementation of the decoding algorithm with advanced CPU instructions. We analyse
computational load and packet loss protection via lab tests and simulations, complementing
the overall analysis of the video streaming system in all its components
The Effective Transmission and Processing of Mobile Multimedia
Ph.DDOCTOR OF PHILOSOPH
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