Improvement on Singular Value Decomposition Vector Quantization.

For high-efficiency image compression, previously, an SVD (singular value decomposition)-based coder was developed using vector quantization, called SVD-VQ. This paper proposes an improved quantization SVD-VQ scheme. For every input subblock, the SVD- VQ coder scalar-quantizes a singular value and vector-quantizes two singular vectors, separately. The SVD-VQ decoder reproduces a subblock as the product of these quantization outputs, but does not necessarily produce a reconstruction with the minimum distortion in an image space. This paper develops a quantization scheme where the minimum-distortion reconstruction is always provided in the original image space and presents its design algorithm. The improved SVD-VQ shows A/N performance improvement of 0.5 - 1.0 dB over the conventional SVD-VQ, and is similar in performance to the adaptive DCT (discrete cosine transform) coder. [ABSTRACT FROM AUTHOR]