Distortion-Controlled Dithering with Reduced Recompression Rate

Dithering is a technique that can improve human perception of low-resolution data by reducing quantization artifacts. We hypothesize that the perceptual prominence of quantization artifacts is proportional to the magnitude of the quantization error autocorrelation vector. Under this hypothesis we derive two parametric dither distributions that trade-off between minimizing mean square error and minimizing an upper bound on the quantization error autocorrelation vector magnitude in the ℓ 1 sense ({f{ₕ_{₁, }} (v) = (v) + (1 -) 12 ({v - { 2}) + (v + { 2}) } }) or ℓ 2 sense ({f{ₕ_{₂, }} (v) = (v) }) where ₐ (v) 1a, - a2 v a2 and ∆ is the width of the quantization region. The application of these distortion-controlling dithers to an example low-rate image recompression problem (using Lena) reveals optimal performance with partial dithering (0 < α ∝ λ < 1) as per Fig. 1 while our novel ℓ 1 -optimized dither produces a new Pareto front for the quality-entropy trade-off shown in Fig. 2.