A lattice-based vector quantizer (VQ) and noiseless code are proposed for transform and subband image coding. The quantization is simple to implement, and no vector codebooks need to be stored. The noiseless code enumerates lattice codevectors based on their (weighted) l(1) norm. A software implementation is able to handle lattice codebooks of size 2(256). The image coding performance is shown to be comparable or superior to the best encoding methods reported in the literature.

We present a fixed-rate lattice VQ (FRLVQ) for subband image coding. The fixed-rate lattice VQ is an extension of the entropy-coded lattice vector quantizer (ECLVQ) [Mohdyusof and Fischer, 1994]. For an error-free channel, image coding using ECLVQ provides competitive performance with other methods. ECLVQ is a variable length coder, hence a single bit error in the encoded bit stream could lead to error propagation resulting in severe degradation in image quality. A fixed-rate lattice VQ is constructed from the ECLVQ by grouping together several vectors as a "macroblock". The macroblock is encoded at a fixed rate, while allowing a variety of encoding rates for each vector in the macroblock. In the event of a transmission error, error propagation is confined to that particular macroblock. In this work we assume the encoded bit stream is transmitted over a binary symmetric channel. The simulation results show that the FRLVQ subband image coding provides competitive performance with other fixed-rate image coding systems. The simulation results also indicate FRLVQ performance degrades gracefully in the presence of channel noise.

A lattice-based vector quantizer (VQ) and noiseless code are proposed for transform and subband image coding. The quantization is simple to implement and no vector codebooks need be stored. The noiseless code enumerates lattice codevectors based on their (weighted) l/sub 1/ norm. A software implementation is able to handle lattice codebooks of size 2/sup 256/. The image coding performance is shown to be comparable or superior to the best encoding methods reported in the literature.>