Error Resilience in a Generic Compressed Video Stream Transmitted over a Wireless Channel Muhammad Bilal

Channel Noise  Markov Process Model  AWGN Model  Transfer Function Model  Hata Model, Akumura Model

Error Correction Methods Redundancy Redundancy  Header information  Motion Vectors  DC coefficient Source Coding Source Coding  Reed Solomon  Hamming Code Channel Equalization Channel Equalization  Channel coding  Channel response

Error Localization Reversible Variable Length Codes Reversible Variable Length Codes Fixed Synchronization Markers Fixed Synchronization Markers

Error Resilience Methods Data Partitioning Data Partitioning SNR scalability SNR scalability Prediction Prediction –Intra coded frame  Copy previous block  DC coefficient prediction –Inter coded frame  Motion Vector Data based prediction

Framework A generic video compressor A generic video compressor –MATLAB implementation –‘MPEG-2 like’ bit stream –Platform for video coding analysis  Compression efficiency  Motion Estimation (offset distortion)  Data Partitioning  Etc –Demonstration of good quality & highly quantized videos

Error Introduction Methods Arbitrary bursts of error in bit stream Arbitrary bursts of error in bit stream –Header loss –RVLC synchronization problem –Need to deal with resynchronization

Error Introduction Methods (contd.) Intelligent error introduction (Macroblock level) Intelligent error introduction (Macroblock level) –Assume bit stream remains synchronized –Error in coefficients/motion vector data –SNR degradation –Demonstration  Error propagation due to motion compensation  Need for ‘I’ frame GDR (Gradual Data Refresh)

Quality Measures Subjective evaluation Subjective evaluation SNR SNR –Deceiving results for some sequences

Analysis Effect of various error concealment methods Effect of various error concealment methods –I Frames  DC Coefficients saved –‘D’ frame  DC Coefficients not saved –Copy previous frame block  Error propagation due to motion compensation

Analysis (contd.) ‘P’ Frames ‘P’ Frames –Dependent on ‘I’ frame (error propagation) –Dependent on content  High motion content (Foreman)  Head & Shoulder (News)  Camera panning (Coastguard) –Motion Vector Data + DCT  DCT data useless without MV  MV data useful without DCT data  demonstration

Analysis (contd.) ‘P’ Frames ‘P’ Frames –Absence of DCT data  Copy motion compensated block  Previous frame non MC block degrades video for high motion content

Analysis (contd.) ‘P’ Frames ‘P’ Frames –Dependency on ‘I’ frames –Perfect ‘I’ frame decoding  DCT data destroyed  MV data available  Demonstration –Seamless video (news) –Acceptable video for many purposes (coastguard, foreman)

Analysis (contd.) Critical Data Critical Data –I Frame  DC coefficients –P Frame  I frame  MV data  Motion Estimation algorithm –Attempt to find the ‘actual’ motion vector

SNR vs BER

Conclusion Error Resilience Error Resilience –Error localization  Parity  Hamming Codes –Redundancy  DC coefficients + MV data  I frame perfect decoding (BW demanding) –Compensate with more number of P frames in GOP

Conclusion (contd.) Data Partitioning Data Partitioning –Critical data positioned close to resynchronization marker  DC coefficients in ‘I’ frames  MV data in ‘P’ frames

Conclusion (contd.) Further compression! Further compression! –Randomly introduce ‘not coded’ blocks  Depend on decoder error concealment scheme  Infrequent ‘not coded’ blocks will result in seamless video decoding

Q&A