Lossless Image Compression by Quantization Feedback in a Content-Driven Enhanced Laplacian Pyramid (Articolo in rivista)

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  • Lossless Image Compression by Quantization Feedback in a Content-Driven Enhanced Laplacian Pyramid (Articolo in rivista) (literal)
Anno
  • 1997-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/83.585234 (literal)
Alternative label
  • Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Franco Lotti (1997)
    Lossless Image Compression by Quantization Feedback in a Content-Driven Enhanced Laplacian Pyramid
    in IEEE transactions on image processing; IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Franco Lotti (literal)
Pagina inizio
  • 831 (literal)
Pagina fine
  • 843 (literal)
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  • http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=585234 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 6 (literal)
Rivista
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  • 13 (literal)
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  • 6 (literal)
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  • Scopu (literal)
  • Google Scholar (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Nello Carrara Research Institute on Electromagnetic Waves (IROE), National Research Council (CNR), Florence, Italy Department of Electronic Engineering, University of Florence, Florence, Italy Nello Carrara Research Institute on Electromagnetic Waves (IROE), National Research Council (CNR), Florence, Italy Nello Carrara Research Institute on Electromagnetic Waves (IROE), National Research Council (CNR), Florence, Italy (literal)
Titolo
  • Lossless Image Compression by Quantization Feedback in a Content-Driven Enhanced Laplacian Pyramid (literal)
Abstract
  • In this paper, the effects of quantization noise feedback on the entropy of Laplacian pyramids are investigated. This technique makes it possible for the maximum absolute reconstruction error to be easily and strongly upper-bounded (near-lossless coding), and therefore, allows reversible compression. The entropy-minimizing optimum quantizer is obtained by modeling the first-order distributions of the differential signals as Laplacian densities, and by deriving a model for the equivalent memoryless entropy. A novel approach, based on an enhanced Laplacian pyramid, is proposed for the compression, either lossless or lossy, of gray-scale images. Major details are prioritized through a content-driven decision rule embedded in a uniform threshold quantizer with noise feedback. Lossless coding shows improvements over reversible Joint Photographers Expert Group (JPEG) and the reduced-difference pyramid schemes, while lossy coding outperforms JPEG, with a significant peak signal-to-noise ratio (PSNR) gain. Also, subjective quality is higher even at very low bit rates, due to the absence of the annoying impairments typical of JPEG. Moreover, image versions having resolution and SNR that are both progressively increasing are made available at the receiving end from the earliest retrieval stage on, as intermediate steps of the decoding procedure, without any additional cost. (literal)
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