Rate-distortion Optimized Mode Selection Based on Multi-channel Realizations Markus Gärtner Davide Bertozzi Classroom Presentation 13 th March 2001

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Overview Hybrid Video Coding Proposed Architecture Multi-channel realizations Performance Measurements: Concealment Techniques Number of Channel Realization Error Propagation Conclusions

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Motion-compensated hybrid coder Intraframe DCT coder Motion compensated predictor Intraframe Decoder Mode Control XORXOR Decoder Encoder

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Proposed Improvements over H.263 H.263 Hybrid Video Coder: Error propagation in the decoder loop neglected Error-free transmission assumed Threshold based mode selection Goals of our approach: Simulation of several channel conditions Prediction of the error incurred at the receiver Rate-Distortion optimized mode selection

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Multiple Channel Realizations Coder Frame Buffer Decoder Inter Intra Channel n Decoder Conceal- ment & Mode Decision Original Encoder n th Channel Realization input output

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Channel Realizations Randomly generated error patterns for each channel Capture different sensitivity of macro-blocks to errors Channel 1 Channel 2 Channel n X X X X X Group of blocks (GOB) Estimate of the real channel conditions (on the average)

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Bit error causes loss of macro block Synchronization markers before each GOB Macro block concealment GOB concealment Concealment of rest of GOB Error Concealment X X X X X X Erroneous macro- blocks are replaced by respective macro-block of previously reconstructed frame

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Distortion Measure Channel 1 Channel N For each Macro-block:

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Mode Selection Decision takes place for each macro-block  selection table Computational complexity Input Frame > < Mode Selection

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Channel Decoder Selection table Coder Mode input Intra: quantized frame Inter: difference signal, motion vectors For each channel : Inter: previous frame buffer content + difference signal Intra: quantized frame Reconstructed Frame

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Experimental Setup Encoder Decoder Channel Quantizer Frame Buffer Dequantizer Distortion Rate

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Performance Measurement (I)

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Performance Measurement (II)

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Number of Realizations

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Error Propagation (I) First I-Frame received in error

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Error Propagation (II) First I-Frame received correctly

Markus Gärtner, Davide Bertozzi: Robust Video coding Stanford University, 13 th March Conclusions Suitability for error-prone environments Better performance than H.263 Reduction of error propagation Limitations Advanced modes of H.263 not considered Computational complexity Application for downloadable multimedia Future work: Sophisticated channel models Implementation of advanced features