Lossy Source Coding and Rate-Distortion Theory

  1. N. Merhav and M. Feder, ``On the cost of universality of block codes for individual sequences,'' Proc. 1994 IEEE Int. Symp. on Information Theory (ISIT `94), p. 263, Trondheim, Norway, June 1994.
  2. N. Merhav, ``A comment on `A rate of convergence result for a universal $D-$semifaithful code','' IEEE Trans. Inform. Theory, vol. 41, no. 4, pp. 1200-1202, July 1995.
  3. N. Merhav, ``On list size exponents in rate-distortion coding,'' IEEE Trans. on Inform. Theory, vol. 43, no. 2, pp. 765-769, March 1997.
  4. N. Merhav and J. Ziv, ``On the amount of statistical side information required for lossy data compression,'' IEEE Trans. Inform. Theory vol. 43, no. 4, pp. 1112-1121, July 1997.
  5. E. Arikan and N. Merhav, ``Guessing subject to distortion,'' IEEE Trans. Inform. Theory, vol. 44, no. 3, pp. 1041-1056, May 1998.
  6. N. Merhav, R. M. Roth, and E. Arikan, ``Hierarchical guessing with a fidelity criterion,'' IEEE Trans. Inform. Theory, vol. 45, no. 1, pp. 330-337, January 1999.
  7. T. Weissman and N. Merhav, ``Tradeoffs between the excess code-length exponent and the excess distortion exponent in lossy source coding,'' IEEE Trans. Inform. Theory, vol. 48, no. 2, pp. 396-415, February 2002.
  8. T. Weissman and N. Merhav, ``On limited-delay lossy coding and filtering of individual sequences,'' IEEE Trans. Inform. Theory, vol. 48, no. 3, pp. 721-733, March 2002.
  9. N. Merhav and I. Kontoyiannis, ``Source coding exponents for zero-delay coding with finite memory,'' IEEE Trans. Inform. Theory, vol. 49, no. 3, pp. 609-625, March 2003.
  10. T. Weissman and N. Merhav, ``On competitive predictability and its relation to rate-distortion theory and to channel capacity theory,'' IEEE Trans. Inform. Theory, vol. 49, no. 12, pp. 3185-3194, December 2003.
  11. Y. Steinberg and N. Merhav, ``On successive refinement for the Wyner-Ziv problem,'' IEEE Trans. Inform. Theory, vol. 50, no. 8, pp. 1636-1654, August 2004.
  12. I. Hen and N. Merhav, ``On the error exponent of trellis source coding,'' IEEE Trans. Inform. Theory, vol. 51, no. 11, pp. 3734-3741, November 2005.
  13. T. Weissman and N. Merhav, ``On causal source codes with side information,'' IEEE Trans. Inform. Theory, vol. 51, no. 11, pp. 4003-4013, Novemeber 2005.
  14. N. Merhav and J. Ziv, ``On the Wyner-Ziv problem for individual sequences,'' IEEE Trans. Inform. Theory, vol. 52, no. 3, pp. 867-873, March 2006.
  15. N. Merhav, ``The generalized random energy model of spin glasses and its application to the statistical physics of code ensembles with hierarchical structures,'' IEEE Trans. Inform. Theory , vol. 55, no. 3, pp. 1250-1268, March 2009.
  16. N. Merhav, ``On the statistical physics of directed polymers in a random medium and their relation to tree codes,'' IEEE Trans. Inform. Theory, vol. 56, no. 3, pp. 1345-1350, March 2010.
  17. A. Reani and N. Merhav, ``Efficient on-line schemes for encoding individual sequences with side information at the decoder,'' Proc. ISIT 2009, Seoul, Korea, June-July 2009. Full version: IEEE Trans. Inform. Theory, vol. 57, no. 10, pp. 6860-6876, October 2011.
  18. N. Merhav, ``Another look at the physics of large deviations with application to rate-distortion theory,'' Technical Report, CCIT Pub. no. 742, EE Pub. no. 1699, August 2009. Also, available in arXiv and here is the conference version, which appears in Proc. ISIT 2010, Austin, Texas, U.S.A., June 2010.
  19. Y. Kaspi and N. Merhav, ``Structure theorem for real-time variable-rate lossy source encoders and memory-limited decoders with side information, Proc. ISIT 2010, Austin, Texas, U.S.A., June 2010. Full version is here (vol. 58, no. 12, pp. 7135-7153, December 2012).
  20. N. Merhav, ``Rate-distortion function via minimum mean square error estimation,'' IEEE Trans. Inform. Theory, vol. 57, no. 6, pp. 3196-3206, June 2011. Comment: There is a slight problem in Theorem 1. The parametric representation therein is guaranteed to hold for the optimal output distribution q. For a general distribution, it gives a lower bound on R_q(D), thus the method proposed can still be used to generate lower bounds. Most of the examples (but the last one) are fine since they are defined with the optimal q. The last example holds as well if the input pdf is the convolution between q and the corresponding generalized Gaussian, so that q is optimum for that pdf. Thanks to Jon Scarlett for drawing my attention.
  21. N. Merhav, ``A statistical-mechanical view on source coding: physical compression and data compression,'' Journal of Statistical Mechanics: Theory and Experiment, P01029, January 2011. doi: 10.1088/1742-5468/2011/01/P01029 [With a certain overlap to no. 18, but with a different emphasis and some other results.]
  22. A. Reani and N. Merhav, ``Data processing lower bounds for scalar lossy source codes with side information at the decoder,'' in ISIT 2012, Cambridge, MA, USA, July 2012. Full version in IEEE Trans. Inform. Theory, vol. 59, no. 7, pp. 4057-4070, July 2013. and can be found here.
  23. Y. Kaspi and N. Merhav, ``On zero-delay lossy source coding with side information at the encoder,'' presented at the 2012 IEEE 27-th Convention of Electrical and Electrnoics Engineers in Israel,, November 14-17, 2012. Full version (with a slightly different title) appeared in IEEE Trans. Inform. Theory, vol. 60, no. 11, pp. 6931-6942, November 2014, and can be found here.
  24. Y. Kaspi and N. Merhav, ``Zero-delay and causal secure source coding,'' IEEE Trans. Inform. Theory, vol. 61, no. 11, pp. 6238-6250, November 2015.
  25. A. Reani and N. Merhav, ``Universal quantization for separate encodings and joint decoding of correlated sources,'' Proc.\ ISIT 2014, pp. 761-765, Honolulu, Hawaii, June-July 2014. Full version in IEEE Trans. Inform. Theory, vol. 61, no. 12, pp. 6465-6474, December 2015. Can be found here.