Vector quantization and signal compression
Vector quantization and signal compression
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
IEEE Transactions on Image Processing
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When the amount of color data is reduced in a lossy compression scheme, the question of the use of a color distance is crucial, since no total order exists in IRn, n 1. Yet, all existing color distance formulae have severe application limitation, even if they are widely used, and not necesseraly within the initial context they have been developed for. In this paper, a manifold learning approach is applied to reduce the dimension of data in a Vector Quantization approach to obtain data expressed in IR. Three different techniques are applied before construct the codebook. Comparaisons with the standard LBG-based VQ method are performed to judge the performance of the proposed approach using PSNR, MS-SSIM and VSNR measures.