Inverse Halftoning of Color Images Using Classified Vector Quantization

Abstract

This paper extends and modifies classified vector quantization (CVQ) to solve the problem of inverse halftoning from error-diffused color images. The process consists of two phases: the encoding and decoding phases. The encoding process needs a codebook for the encoder which transforms a smoothed halftoned image to a set of codewords. The decoding process also requires a different codebook for the decoder which reconstructs a color image from a set of codewords. According the relationships between these two codebooks, we modified the traditional generalized Lloyd algorithm to fit our purpose. Using CVQ, the reconstructed color image can be stored in compressed form and no further compression may be required. This is different from the existing algorithms which reconstruct a monochrome image in an uncompressed form. The bit-rate is about 0.96 bits per pixel for encoding a reconstructed color image. As far as we know, this paper is the first of its kind to solve the inverse halftoning problem of color images.