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DCT and DST Based Image Compression for 3D Reconstruction

3D Research volume 8, Article number: 5 (2017) Cite this article

Abstract

This paper introduces a new method for 2D image compression whose quality is demonstrated through accurate 3D reconstruction using structured light techniques and 3D reconstruction from multiple viewpoints. The method is based on two discrete transforms: (1) A one-dimensional Discrete Cosine Transform (DCT) is applied to each row of the image. (2) The output from the previous step is transformed again by a one-dimensional Discrete Sine Transform (DST), which is applied to each column of data generating new sets of high-frequency components followed by quantization of the higher frequencies. The output is then divided into two parts where the low-frequency components are compressed by arithmetic coding and the high frequency ones by an efficient minimization encoding algorithm. At decompression stage, a binary search algorithm is used to recover the original high frequency components. The technique is demonstrated by compressing 2D images up to 99% compression ratio. The decompressed images, which include images with structured light patterns for 3D reconstruction and from multiple viewpoints, are of high perceptual quality yielding accurate 3D reconstruction. Perceptual assessment and objective quality of compression are compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results show that the proposed compression method is superior to both JPEG and JPEG2000 concerning 3D reconstruction, and with equivalent perceptual quality to JPEG2000.

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Authors and Affiliations

  1. GMPR-Geometric Modelling and Pattern Recognition Research Group, Sheffield Hallam University, Sheffield, UK

    Mohammed M. Siddeq & Marcos A. Rodrigues

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  1. Mohammed M. Siddeq
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  2. Marcos A. Rodrigues
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Correspondence to Mohammed M. Siddeq.

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Siddeq, M.M., Rodrigues, M.A. DCT and DST Based Image Compression for 3D Reconstruction. 3D Res 8, 5 (2017). https://doi.org/10.1007/s13319-017-0116-0

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  • DOI: https://doi.org/10.1007/s13319-017-0116-0

Keywords

  • DCT
  • DST
  • High frequency minimization
  • Binary search algorithm