Abstract

Wyner-Ziv video coding (WZVC) is a fast emerging video coding technique for wireless video sensor networks. WZVC moves the complexity from the encoder (sensor) to the decoder (receiver). This thesis proposes few enhancements to solve challenging problems in WZVC, namely 1) handling impairments of a fading wireless channel in WZVC environment, 2) investigating rate penalty of WZVC in wireless fading channels, 3) adaptive encoder rate control using inter-frame cross-correlation properties, and 4) better side information estimation with a bit-based noise variance computation technique. In case (1), the decoder metric values in WZVC are calculated with respect to the correlation noise, channel noise, and fading. Multiple input multiple output (MIMO) diversity scheme is studied with WZVC for improving the reconstructed video output. Simulation results show that the average peak signal to noise ratio (PSNR) of Foreman video is improved by ≈ 3 dB with the configuration of 2I4O compared to single input single output (SISO) channel at SNR = 2 dB. In case (2), expressions for the rate penalty are analytically derived under different wireless channel conditions. WZVC scheme with receiver diversity (WZVC-RD) is proposed and also demonstrated that it alleviates the channel-induced rate penalty. Simulation results show that with adequate diversity, the channel induced rate penalty can be almost completely eliminated, i.e., the average rate penalty is reduced to be less than 20 Kbps in WZVC-RD for Foreman video at SNR = 2 dB. In case (3), theoretical rate-distortion behavior of conditional decoding is compared with that of the practical WZVC. An encoder rate control algorithm is proposed with respect to the cross-correlation threshold (CCTH) at the decoder, where the definition of the threshold is derived based on cross-correlation statistics of the key frames. Additionally, an adaptive threshold algorithm (ATHA) is proposed to improve the PSNR versus rate relationship. In case (4), correlation noise variance estimation is proposed with respect to the bit pattern of each pixel; it is named as “bit-based noise variance”. PSNR improvement of ≈ 4 dB is observed for the Foreman video frames with higher correlation noise.

Highlights

  • These days, a large proportion of the world’s population are forced to uses image, audio, and video coding technologies in their day to day lives

  • This section discusses the simulation results of Wyner-Ziv video coding (WZVC) with noiseless, additive white Gaussian noise (AWGN), and wireless fading channels; here the reconstructed WZ video quality is compared with different channel conditions

  • The received video frames are investigated in different channel conditions sucha as noiseless channel, AWGN and fading channel, here Foreman, Carphone and Akiyo video sequences are considered in the simulations

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Summary

Introduction

In WZVC, X and Y are correlated video frames; Y is side information, which is a noisy version of WZ frame X. From errorcontrol coding (ECC), the encoder needs to transmit the parity bit sequence of an input video frame X, and decoder reconstructs X (X′) with these received parity bits and side information Y [58]. The β has been computed over the entire video sequence off line at the encoder before the Wyner-Ziv coding procedure starts [4] This computed β is kept constant throughout the decoding of all the Wyner-Ziv frames [4]. The bit-based variance can be used to compute the soft-value of side information, which is used as the received systematic value at the decoder end. This chapter is organized as follows: Section 6.2 proposes a technique for correlation noise estimation between the WZ frame and the side information.

Wyner-Ziv Video Coding
Wyner-Ziv Video Coding in Practice
Wyner- Ziv Video Coding and Correlation Noise
Encoder Rate Control in WZVC
Thesis Contribution
WZVC in Wireless Channel Environment
Rate Penalty Due to the Wireless Channel and Proposed Improvement
Encoder Rate Control in WZVC with Cross-Correlation Threshold (CCTH)
Correlation Noise Estimation in WZVC
Thesis Organization
Encoder 1
Practical Slepian-Wolf Model
Wyner-Ziv Coding
Turbo Based WZVC
Quantization
Turbo Encoder-Decoder
Side Information Generation
Reconstruction
Video Quality Assessment
Feedback Channel and Rate Control
Correlation Noise Between X and Y
Encoder Correlation Noise Models
Decoder Correlation Noise Models
Transmission Channel
Additive White Gaussian Noise Channel (AWGN)
Fading Channel
2.10 Multipath Fading
2.10.1 Wireless Channel Model
Theoretical Motivations and Contributions
Channel Pi2
Theory
Bit-plane Extractor
Turbo Coder
Wireless Channel
MIMO Wireless Channel
WZVC over MIMO Wireless Channel
Reconstructed WZ Frame of the WZVC with Different Channel Conditions
42 SNR at 2 dB
Improvement of WZVC with Wireless Channel Environment
42 SNR at 2dB
Summary
WZC in Noiseless Channel
WZC in AWGN Channel
Proposed WZVC with Receiver Diversity (WZVCRD)
Rate at WZVC-RD
Results
Conditional Rate Distortion
Relationship Between ρXY and RX/Y
Practical WZVC
Observation of Cross-Correlations
Observations of Rate-Distortion
Coast Guard
Cross-correlation Thresholds and Rate Control Algorithm
Cross-correlation Thresholds (CCTHs)
Mean as The Threshold
Positive and Negative Thresholds
Adaptive Threshold (ATH)
Rate Control Algorithm
Rate Control Based on Mean (E)
Rate Control Based on PTH and NTH
Rate Control Based on Adaptive Thresholds APTH and ANTH
Performance Analysis
Correlation Noise Variance Estimation
Results and Discussion
Conclusions
Future Work
Full Text
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