DCT and DST based Image Compression for 3D Reconstruction

SIDDEQ, Mohammed and RODRIGUES, Marcos (2017). DCT and DST based Image Compression for 3D Reconstruction. 3D Research, 8 (5), 1-19.

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Rodrigues DCT and DST based Image Compression for 3D Reconstruction.pdf - Accepted Version
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Official URL: http://link.springer.com/article/10.1007%2Fs13319-...
Link to published version:: 10.1007/s13319-017-0116-0

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.

Item Type: Article
Research Institute, Centre or Group: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Identification Number: 10.1007/s13319-017-0116-0
Depositing User: Marcos Rodrigues
Date Deposited: 06 Feb 2017 11:11
Last Modified: 01 Apr 2017 19:11
URI: http://shura.shu.ac.uk/id/eprint/15146

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