Selectively tree-structured vector quantizer using Kohonen neural network

The computational complexity and image fidelity are two key issues of vector quantization (VQ), especially for real-time applications. This paper proposes a three-phase self-organizing feature map (TPSOFM) algorithm to design selectively three-level tree-structured codebooks. The computational complexity during the coding process is reduced by a factor of 20 over a full search. A ten times speed up in training time is also achieved. The degradation of the reconstructed image quality is less than 0.38 dB in PSNR while the bit rate is reduced.