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H.264 Rate Estimation

H.264 Rate Estimation. EE398 - Image Communication Project Binh Ho, Hoi Wong, Harold Nyikal Advisor: David Varodayan . Outline. Objective Estimate high resolution H.264 encoder output rate from low resolution output rate statistics Approach Results Empirical data, patterns, performance

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H.264 Rate Estimation

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  1. H.264 Rate Estimation EE398 - Image Communication Project Binh Ho, Hoi Wong, Harold Nyikal Advisor: David Varodayan

  2. Outline • Objective • Estimate high resolution H.264 encoder output rate from low resolution output rate statistics • Approach • Results • Empirical data, patterns, performance • Conclusion

  3. Objective Full Res Video Encoder (H.264) Rhigh We want to finda good estimate of Estimator Low Res Video Encoder (H.264) Rlow

  4. Empirical Approach / Experiment • Form a training set • 5 video clips (YUV) • Break each clip into groups of 8 frames (GOPs). • Carry out spatial and temporal down-sampling • Encode the 8-frames clips at original size, and also the down-sampled versions, using IPPPPPPP. • Find relationships between Rlow and Rhigh from the output statistics given by the encoder.

  5. Down-sampling • To obtain a low complexity version of the original video, we can • Down-sample it in time (frame skip) • Down-sample it in space • Combination • We will show how down-sampling in each dimension affect the reliability of the estimation.

  6. Temporal down-sampling • IPPPPPPP becomes I_P_P_P_ • Total rate = I-Slice rate + P-Slice rate + ε • I bit-rates remain the same. • P bit-rates are very predictable, but degrades with high sampling ratio. ε:= some minor stuff like overheads

  7. Spatial Down-sampling • I-Slice bit-rates are fairly predictable • Loss of data correlations/redundancies • Same basic data pattern • P-Slice bit-rates are somewhat harder to predict • Spatial down-sampling affects the mode decision • Shrinks movements, yet the minimum block size remains the same • Dependent on nature of the video clips • We also combined both temporal and spatial down-sampling by a factor of 2 each, thus reducing complexity by a factor of 8.

  8. Total Bit-rate

  9. I slice bit rate

  10. P slice bit-rate

  11. Trends and Model • From the plots, we can see affine relationships among low resolution and high resolution rates. • Perform least squares fit. • When we involve spatial down-sampling, the ‘affine’ relationships in the P-Slice rate starts to break down into regions with different y-intercepts. • We model this phenomena in our estimator.

  12. P-Slice Rate

  13. Estimator Performance

  14. Prediction Trace

  15. News Container Mom Akiyo Salesman CoastGuard Hall Relative Error (Test Set)

  16. Conclusion • We built an estimator to predict the bit rate for H.264 encoding a full resolution video based on rate statistics of a low resolution version. • Estimator derived from least squares fitting of empirical data. • Temporal down-sampling gives strong linear bit-rate relationship. • Spatial down-sampling retains linear relationship but with higher deviations.

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