Hyperspectral image coding using Spectral Prediction Modelling in HEVC coding framework
Rui Dusselaar, Manoranjan Paul, Terry R. J. Bossomaier · 2015
A novel coding framework using Spectral Prediction Modelling (SPM) in the latest High Efficiency Video Coding (HEVC) framework for Hyperspectral (HS) images is proposed in this paper. A HS image presents a wealth of data where every pixel is considered as a vector in a multidimensional space. By quantitative comparison and analysis of pixel vector's distribution along spectral bands, we can conclude that modelling can solve the distribution and correlation of pixel vectors in a certain range of bands. We estimate the current spectral band by extracting an instant spectral band of the HS image using Gaussian Mixture-based Modelling via the correlation of previous bands. The estimated current spectral band named as the common informatics wavelength (CIW) image is used as the additional reference to encode the residual of the current band with either the CIW or the previous band followed by the HEVC coding framework. Every spectral band of the HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream HS encoders including JPEG2000, JPEG, PCA-DCT, HEVC-intra, Set Partitioning in Hierarchical Trees three dimensional (SPIHT-3D) and Adaptively Scanned Wavelet Difference Reduction (ASWDR). The experimental results fully justified by different types of HS datasets. The performance of the proposed method has outperformed the other encoding algorithms in terms of Rate- Distortion performance of HS image compression.