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Improving Scene Cut Quality for Real-Time Video Decoding

Improving Scene Cut Quality for Real-Time Video Decoding. Giovanni Motta, Brandeis University James A. Storer, Brandeis University Bruno Carpentieri, Universita’ di Salerno. Outline. Introduction H.263+ and TMN-8 Rate Control Problem Description

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Improving Scene Cut Quality for Real-Time Video Decoding

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  1. Improving Scene Cut Quality for Real-Time Video Decoding Giovanni Motta, Brandeis University James A. Storer, Brandeis University Bruno Carpentieri, Universita’ di Salerno

  2. Outline • Introduction • H.263+ and TMN-8 Rate Control • Problem Description • Optimal Algorithm based on Dynamic Programming • Experimental Results • Conclusions and Future Research

  3. Introduction • High variability in video sequences may cause the encoder to skip frames • Frame skipping occurs after a “scene cut” (i.e. when MC-prediction model fails) • If the encoder has some look-ahead capability it is possible to improve quality in proximity of scene cuts

  4. H.263+ Video Encoding • State of the art Video Coding • MC-prediction and DCT coding • I and P macroblocks • Rate control

  5. TMN-8 Rate Control • I/P Frame and MB decisions • Target bit rate for each frame • RD optimized bit allocation for MBs • Buffer control

  6. Problem Description • Bits per frame (std100.qcif)

  7. Problem Description • PSNR and Bits per frame across a scene cut

  8. Problem Description • Frame n has several “I” macroblocks • Encoder is forced to skip n+1, n+2, n+3 • Frame n-1 frozen on receiver’s display • Frame n+4 has a large prediction error • Encoder forced to skip frame n+5

  9. Basic Idea • Avoid extra skipping and improve quality by selecting which frame should be encoded after a scene cut • Assumption: Encoder has look-ahead capability

  10. Simplified approach TMN-8 behavior Last frame of the skipped sequence encoded

  11. Simplified approach • PSNR and Bits per frame across a scene cut

  12. Optimal Algorithm • Minimizes the number of skipped frames • Generalization of the text-paragraphing algorithm • Assumptions: • When the quality of F[i-j] is fixed to Q, the cost P[i, j] of predicting F[i] from F[i-j], is independent of how F[i-j] is encoded • P[i, j]  P[i, j+1]  P[i, 0], 1  j  d

  13. Optimal Algorithm • Compute P[i, 0] for each frame • Compute P[i, j] for 1  j  d • Build (right to left) two matrices • R[i, j]: maximum residual capacity when F[i], …, F[n] are encoded so that the first frame that is not skipped is predicted by F[i-j] • S[i,j]: number of skipped frames corresponding to residual capacity R[i, j] • Time is O(d2n) = O(n) (constant d  7)

  14. Test Sequences Std and Std100: concatenation of standard test sequences Commercials: Sampled TV commercials

  15. Experimental Results • Gain in Bit/PSNR in proximity of scene cuts (simplified method)

  16. Experimental Results • Gain on whole sequence (simplified method)

  17. Conclusions • Simple yet effective method to improve quality in proximity of scene cuts • Experiments with simplified method show improvements of 14-30% (in Bit/PSNR) • Suitable for encoders of the MPEG family, provided that encoder has look-ahead capability • Decoding is unaffected

  18. Future Research • Assess quality improvement when using optimal algorithm • Experiment with progressive transmission to eliminate frozen frame displayed by the decoder

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