IMAGE-CODING USING WAVELET TRANSFORMS AND ENTROPY-CONSTRAINED TRELLIS-CODED QUANTIZATION

The discrete wavelet transform has recently emerged as a powerful tech nique for decomposing images into various multi-resolution approximati ons. Multi-resolution decomposition schemes have proven to be very eff ective for high-quality, low bit-rate image coding, In this work, we i nvestigate the use of entropy-constrained trellis-coded quantization ( ECTCQ) for encoding the wavelet coefficients of both monochrome and co lor images, ECTCQ is known as an effective scheme for quantizing memor yless sources with low to moderate complexity, The ECTCQ approach to d ata compression has led to some of the most effective source codes fou nd to date for memoryless sources. Performance comparisons are made us ing the classical quadrature mirror filter bank of Johnston and nine-t ap spline filters that were built from biorthogonal wavelet bases, We conclude that the encoded images obtained from the system employing ni ne-tap spline filters are marginally superior although at the expense of additional computational burden. Excellent peak-signal-to-noise rat ios are obtained for encoding monochrome and color versions of the 512 x 512 ''Lenna'' image, Comparisons with other results from the litera ture reveal that the proposed wavelet coder is quite competitive.