A Fast Approach for Adaptive Conventional Recursive Least-Squares Predictor in Lossless Compression of Hyperspectral Images

KARACA A. C., GÜLLÜ M. K.

25th Signal Processing and Communications Applications Conference (SIU), Antalya, Türkiye, 15 - 18 Mayıs 2017 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2017.7960453
  • Basıldığı Şehir: Antalya
  • Basıldığı Ülke: Türkiye
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Recursive least-squares (RLS) based prediction methods are very popular in lossless compression of hyperspectral imaging. Adaptive selection of number of bands used in the prediction in RLS based methods increases compression performance significantly. However, this process brings additional computational load. In this work, a sample reduction based fast adaptive method to determine the number of bands required for prediction is proposed. Performance of the proposed method is compared to the-state-of-the-art methods in terms of bitrates and computation times and obtained results are discussed.