A still image encoder based on adaptive resolution vector quantization realizing compression ratio over 1/200 featuring needless calculation elimination architecture

We have developed an advanced vector quantization (VQ) encoding hardware for still image encoding systems. By utilizing needless calculation elimination method, computational cost of VQ encoding is reduced to 40% or less, while maintaining the accuracy of full-search VQ. We have also developed a still image compression algorithm based on adaptive resolution VQ (AR-VQ), which realizes compression ratio over 1/200 while maintaining image quality. We have successfully implemented these two technologies into a still image encoding processor. The processor can compress still image of 1600/spl times/2400 pixels within one second, which is 60 times faster than software implementation on current PCs.

[1]  K. Kotani,et al.  A parallel vector-quantization processor eliminating redundant calculations for real-time motion picture compression , 2000, IEEE Journal of Solid-State Circuits.

[2]  T. Morimoto,et al.  A fully-parallel vector quantization processor for real-time motion picture compression , 1997, 1997 IEEE International Solids-State Circuits Conference. Digest of Technical Papers.

[3]  Abraham Lempel,et al.  Compression of individual sequences via variable-rate coding , 1978, IEEE Trans. Inf. Theory.

[4]  John B. O'Neal Differential pulse-code modulation (PCM) with entropy coding , 1976, IEEE Trans. Inf. Theory.