Excess distortion in lossy compression: Beyond one-shot analysis

The problem of finite-blocklength lossy compression under an excess-distortion constraint is considered. If the blocklength constraint comes from the length of the source sequence itself, the excess rate needed above the rate-distortion function decays inversely proportional to the square root of the blocklength, according to second-order (dispersion) analysis. We consider a different case, where the source emits a long sequence, but shorter sub-sequences are considered for reasons such as delay, complexity and smoothness of the reconstruction fidelity. We analyze the redundancy of the rate with respect to different constraints. We show that the rate redundancy with respect to the processing blocklength, i.e. the dimension of the quantizer used, decays much faster than the dispersion analysis suggests. Thus, one may use much shorter source codes without sacrificing second-order performance.

[1]  Zhen Zhang,et al.  The redundancy of source coding with a fidelity criterion: 1. Known statistics , 1997, IEEE Trans. Inf. Theory.

[2]  Yuval Kochman,et al.  The Dispersion of Lossy Source Coding , 2011, 2011 Data Compression Conference.

[3]  Katalin Marton,et al.  Error exponent for source coding with a fidelity criterion , 1974, IEEE Trans. Inf. Theory.

[4]  T. Hoglund Sampling from a Finite Population. A Remainder term Estimate , 2017 .

[5]  Bin Yu,et al.  A rate of convergence result for a universal D-semifaithful code , 1993, IEEE Trans. Inf. Theory.

[6]  Anant Sahai Why Do Block Length and Delay Behave Differently if Feedback Is Present? , 2008, IEEE Transactions on Information Theory.

[7]  Yuval Kochman,et al.  Dispersion theorems via second order analysis of functions of distributions , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[8]  Sergio Verdú,et al.  Variable-Length Compression Allowing Errors , 2014, IEEE Transactions on Information Theory.

[9]  Toby Berger,et al.  Rate distortion theory : a mathematical basis for data compression , 1971 .

[10]  Imre Csiszár,et al.  Information Theory - Coding Theorems for Discrete Memoryless Systems, Second Edition , 2011 .

[12]  Sergio Verdú,et al.  Fixed-Length Lossy Compression in the Finite Blocklength Regime , 2011, IEEE Transactions on Information Theory.

[13]  Gregory W. Wornell,et al.  On uncoded transmission and blocklength , 2012, 2012 IEEE Information Theory Workshop.