1995/01/01 by Glen P. Abousleman, Michael W. Marcellin, B.R. Hunt · 2 citations
Computer Science · Mathematics · #Advanced Data Compression Techniques #Algorithm #Artificial intelligence #Coding (social sciences) #Computer science #Computer vision #Data compression #Decorrelation #Digital Filter Design and Implementation #Discrete cosine transform #ENCODE #Entropy (arrow of time) #Entropy encoding #Huffman coding #Hyperspectral imaging #Image (mathematics) #Image and Signal Denoising Methods #Image compression #Image processing #Mathematics #Quantization (signal processing) #Statistics #Transform coding #Trellis quantization
paper · doi:10.1109/36.368225
openalex publication_date 1995/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Two systems are presented for compression of hyperspectral imagery which utilize trellis coded quantization (TCQ). Specifically, the first system uses TCQ to encode transform coefficients resulting from the application of an 8/spl times/8/spl times/8 discrete cosine transform (DCT). The second systems uses DPCM to spectrally decorrelate the data, while a 2D DCT coding scheme is used for spatial decorrelation. Side information and rate allocation strategies are discussed. Entropy-constrained code-books are designed using a modified version of the generalized Lloyd algorithm. These entropy constrained systems achieve compression ratios of greater than 70:1 with average PSNRs of the coded hyperspectral sequences exceeding 40.0 dB.>