Multispectral image compression by cluster-adaptive subspace representation

2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010Multispectral imaging has attracted much interest in color science area, for its ability in providing much more spectral information than 3-channel color images. Due to the huge data volume, it is necessary to compress multispectral images for efficient transmission. This paper proposes a framework for spectral compression of multispectral image by using clusteradaptive subspaces representation. In the framework, multispectral image is initially segmented by hierarchical analysis of the transform coefficients in the global subspace, and then ambiguous pixels are identified and classified into proper clusters based on linear discriminant analysis. The dimensionality of each adaptive subspace is determined by specified reconstruction error level, followed by further cluster splitting if necessary. The efficiency of the proposed method is verified by experiments on real multispectral images.Institute of Textiles and ClothingRefereed conference pape

Density Estimation for Color Images

Color histograms computed from the normalized and hue color space are negatively affected by sensor noise due to the instability of these color space transforms at many RGB values. To suppress the effect of sensor noise, in this paper density estimations are computed using variable kernels. To that end, models are proposed for the propagation of sensor noise through the normalized and hue colors. As a result, not only the hue and normalized color values are known, but also the associated uncertainty. This two-fold information is used to derive the parameterization of the variable kernel used for the density estimation. It is empirically verified that the proposed method compares favorably to the traditional histogram