Interpolative DPCM

Recent results have established that DPCM is quite inefficient when encoding tightly correlated autoregressive sources at low rates. This work presents a slightly more complex code generator based on interpolation for rate 1 encoding of such sources and evaluates its performance via simulation for Gaussian and Laplacian distributed general stationary autoregressive sources. Improvements in the range of 3 dB over the LMS predictive quantizer for speech-like correlated sources at rate 1 have been realized with no tree searching. Another 1 dB is gained with a moderate tree searching. The code generator is less sensitive to the quantizer step size and a locally mismatched source than DPCM, two desirable features for encoding quasi-stationary sources.