Image data compression using autoregressive time series models
Pattern RecognitionPublished 1 January 1979
Edward J. Delp, Rangasami L. Kashyap, O. Robert Mitcheli
Citations81
SJR quartileQ1
SJR score2.06
SNIP2.67
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TL;DR
Results obtained indicate this two-dimensional image model is formulated using a seasonal autoregressive time series and could be used to code textures for low bit rates or be used in an application of generating compressed background scenes.
Abstract
A two-dimensional image model is formulated using a seasonal autoregressive time series. With appropriate use of initial conditions, the method of least squares is used to obtain estimates of the model parameters. The model is then used to regenerate the original image. Results obtained indicate this method could be used to code textures for low bit rates or be used in an application of generating compressed background scenes. A differential pulse code modulation (DPCM) scheme is also demonstrated as a means of archival storage of images along with a new quantization technique for DPCM. This quantization technique is compared with standard quantization methods.
Keywords
Computer Science
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