Abstract:
Multiview Video (MVV) is one of the emerging technology in recent years. The
concept of MVV is becoming very important, during the implementation of 3D
systems to enhancing the viewing of high-resolution videos and images from dif ferent angles. Streaming of MVV over a wireless sensor network (WSN) is very
susceptible to whole-frame losses due to wireless channel errors and low-resolution
cameras are used as sensor nodes in WSN. Along this, different studies try to
develop error concealment techniques for MVC.
In this thesis, we propose the identification of a lost frame method by using a
Machine Learning (ML) models and a recovery algorithm for a lost frames of MVV
in WSN by using Long Short Term Memory (LSTM) regression method. The
detection method uses video and image quality assessment techniques to extract
the features from the MVV frame sequences. The recovery method uses motion
estimation and disparity estimation techniques to extract and select features for
LSTM regression algorithm from MVV frame sequences.
The performance of the proposed methods was analyzed on different MVV se quences. The experimental results of the proposed detection method have scored
93.12 % accuracy to detect the lost frames in MVV sequences. And the proposed
LSTM based recovery algorithm has the capability to improve the video quality
of MVC at the decoder side. Compared with the recent methods the proposed
method exceeded the average Peak Signal to Noise Ratio (PSNR) upto 2.47dB.
The complexity of the proposed method also acceptable. This study is expected
to open up new perspectives on how to detect and restore the missing frames in
MVV transmission at real-time
