1D representation of Laplacian eigenmaps and dual k‐nearest neighbours for unified video coding. Issue 10 (19th June 2020)

• Record Type: Journal Article
• Authors: Li, Honggui
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 2156
• Page End: 2165
• Publication Date: 2020-06-19
• Subjects: video coding -- eigenvalues and eigenfunctions -- image resolution; Laplacian eigenmaps -- k‐nearest neighbours -- unified video coding -- LEM -- related embedding -- reconstruction algorithm -- one‐dimensional representation -- extremely low bit -- BPP -- high‐dimensional data space -- low‐dimensional representation space -- nonlinear dimensionality reduction methods -- neighbour properties -- ERA -- peak‐signal‐to‐noise ratio -- multiview video coding -- low bitrate transmission -- highly efficient video coding; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1119
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study proposes a framework of video coding based on Laplacian eigenmaps (LEM) and its related embedding and reconstruction algorithm (ERA). Firstly, a one‐dimensional (1D) representation of LEM is adopted to achieve an extremely low bit per pixel (BPP). Secondly, dual k ‐nearest neighbours, which keeps neighbour relationships both in high‐dimensional data space and low‐dimensional representation space and overcomes the disadvantage of classical non‐linear dimensionality reduction methods which cannot preserve the neighbour properties in both of the spaces, based ERA of LEM is employed to gain extraordinarily high peak‐signal‐to‐noise ratio (PSNR). Thirdly, a unified framework of video coding is fit for intra‐frame, inter‐frame and multi‐view video coding. Finally, it is evaluated by simulation experiments that, in the situation of low bitrate transmission, the proposed method can attain better performance of BPP and PSNR than that of the state‐of‐the‐art methods, such as highly efficient video coding.