Multimedia Tools and Applications

, Volume 76, Issue 6, pp 8651–8676 | Cite as

1D representation of locally linear embedding for image prediction



Image prediction is a very important step in image and video coding. LLE (locally linear embedding) is a famous algorithm of NLDR (nonlinear dimensionality reduction), and it is capable of projecting high dimensional image blocks into a low dimensional space of embedding. This paper is concerned with the image prediction by 1D representation of LLE algorithm. Two LLE algorithms have been studied. One is the general LLE algorithm, the other is the proposed distance-keeping based LLE algorithm, which has the merit of preserving the distance property in low dimensional space. 1D representation of LLE algorithms can hugely improve the CR (compression ratio). The training input and output of LLE algorithms are employed as training pair for ERA (embedding and reconstruction algorithm) of testing samples, and the training pair is as large as possible to overcome the inherent disadvantage of classical algorithms for image prediction, which only utilize the adjacent image blocks. Three stable ERAs have been proposed. The first is general ERA, the second is nearest neighbor based ERA, and the third is machine learning based ERA. The nearest neighbor based ERA has the best performance if the training samples are sufficient, while the machine learning based ERA has the best performance if the training samples are insufficient. Three DLAs (dictionary learning algorithms) for selecting training samples are presented. The first is exemplars based DLA, the second is K-means clustering based DLA, and the third is sparse representation based DLA. The K-means clustering based DLA has the best performance. A unified framework for intra-frame, inter-frame, multi-view, 3D and multi-view 3D image prediction, has been built according to the proposed algorithms. The performance of proposed algorithms for image prediction has been evaluated by simulation experimentations. The results of simulation experiments indicate that proposed algorithms are able to gain very high PSNR (peak signal to noise ratio). The results of simulation experiments also reveal that 1D representation of distance-keeping based LLE algorithm, machine learning based ERA, and K-means clustering based DLA are very effective and efficient for image prediction.


Locally linear embedding Nonlinear dimensionality reduction Embedding and reconstruction Machine learning Dictionary learning Image prediction Image coding Video coding 


  1. 1.
    Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC Superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282CrossRefGoogle Scholar
  2. 2.
    Ankur A, Bill T (2008) Multilevel image coding with hyperfeatures. Int J Comput Vis 78(1):15–27CrossRefGoogle Scholar
  3. 3.
    Blasi SG, Mrak M, Izquierdo E (2015) Frequency-domain intra prediction analysis and processing for high-quality video coding. IEEE Trans Circ Syst Video Technol 25(5):798–811CrossRefGoogle Scholar
  4. 4.
    Changshui Z (2004) Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction. Pattern Recogn 37:325–336MATHCrossRefGoogle Scholar
  5. 5.
    Chen J, Zengwei J, Cao H, Ma B (2013) Accelerated implementation of adaptive directional lifting-based discrete wavelet transform on GPU. Elsevier, Signal Processing: Image Communication 28:1202–1211Google Scholar
  6. 6.
    Cherigui S, Guillemot C, Thoreau D, Guillotel P, Perez P (2013) Correspondence map-aided neighbor embedding for image intra prediction. IEEE Trans Image Process 22(3):1161–1174MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chuohao Y, Parvez A, Kannan R (2011) Coding of image feature descriptors for distributed rate-efficient visual correspondences. Int J Comput Vis 94(3):267–281MATHCrossRefGoogle Scholar
  8. 8.
    De Abreu A, Frossard P, Pereira F (2015) Optimizing multiview video plus depth prediction structures for interactive multiview video streaming. IEEE J Sel Top Signal Process 9(3):487–500CrossRefGoogle Scholar
  9. 9.
    Dey B, Kundu M (2015) Efficient foreground extraction from HEVC compressed video for application to real-time analysis of surveillance ‘big’ data. IEEE Trans Image Process 24(11):3574–3585MathSciNetCrossRefGoogle Scholar
  10. 10.
    Farid MS, Lucenteforte M, Grangetto M (2015) Panorama view with spatiotemporal occlusion compensation for 3D video coding. IEEE Trans Image Process 24(1):205–219MathSciNetCrossRefGoogle Scholar
  11. 11.
    Genaro D-S, German C-D, Principe JC (2012) Locally linear embedding based on correntropy measure for visualization and classification. Neurocomputing 80:19–30CrossRefGoogle Scholar
  12. 12.
    Goulermas JY, Liatsis P, Zeng X-J, Cook P (2007) Density-driven generalized regression neural networks (DD-GRNN) for function approximation. IEEE Trans Neural Netw 18(6):1683–1696CrossRefGoogle Scholar
  13. 13.
    Gudivada VN, Baeza-Yates R, Raghavan VV (2015) Big data: promises and problems. Computer 48(3):20–23CrossRefGoogle Scholar
  14. 14.
    Guillemot C, Cherigui S, Thoreau D (2013) K-NN search using local learning based on regression for neighbor embedding-based image prediction. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 2006–2010. Vancouver, CanadaGoogle Scholar
  15. 15.
    Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Hu W, Cheung G, Ortega A (2015) Intra-prediction and generalized graph Fourier transform for image coding. IEEE Sig Process Lett 22(11):1913–1917CrossRefGoogle Scholar
  17. 17.
    Jiang C, Nooshabadi S (2015) A scalable massively parallel motion and disparity estimation scheme for multiview video coding. IEEE Trans Circuits Syst Video Technol PP(99):1–1.Google Scholar
  18. 18.
    Kamisli F (2015) Block-based spatial prediction and transforms based on 2D Markov processes for image and Video compression. IEEE Trans Image Proc 24(4):1247–1260MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kouropteva O, Okun O, Pietikainen M (2005) Incremental locally linear embedding. Pattern Recogn 38(10):1764–1767MATHCrossRefGoogle Scholar
  20. 20.
    Martin A, Fuchs J-J, Guillemot C, Thoreau D (2007) Sparse representation for image prediction. In: Proceedings of European Signal Processing Conference, pp 1255–1259. Pozna, PolandGoogle Scholar
  21. 21.
    Merkle P, Muller K, Marpe D, Wiegand T (2015) Depth intra coding for 3D video based on geometric primitives. IEEE Trans Circuits Syst Video Technol PP(99):1–1Google Scholar
  22. 22.
    Mikhail Belkin, Partha Niyogi (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in neural information processing systems 14:586–691. MIT Press, MassachusettsGoogle Scholar
  23. 23.
    Monniga ND, Fornberga B, Meyerb FG (2014) Inverting nonlinear dimensionality reduction with scale-free radial basis function interpolation. Appl Comput Harmon Anal 37(1):162–170MathSciNetCrossRefGoogle Scholar
  24. 24.
    Nichols JM, Bucholtz F, Nousain B (2011) Automated, rapid classification of signals using locally linear embedding. Expert Syst Appl 38(10):13472–13474CrossRefGoogle Scholar
  25. 25.
    Philips P (2002) The gait identification challenge problem: data sets and baseline algorithm. In: Proceedings of international conference on pattern recognition, vol 1, pp 385–388. Quebec City, QC, CanadaGoogle Scholar
  26. 26.
    Purica A, Mora E, Pesquet-Popescu B, Cagnazzo M, Ionescu B. (2015) Multiview plus depth video coding with temporal prediction view synthesis. IEEE Trans Circuits Syst Video Technol PP(99):1–1Google Scholar
  27. 27.
    Roweis ST (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  28. 28.
    Ruifeng S, Wensheng C, Xueguang S (2014) Variable selection based on locally linear embedding mapping for near-infrared spectral analysis. Chemom Intell Lab Syst 131:31–36CrossRefGoogle Scholar
  29. 29.
    Saul L, Roweis S (2003) Think globally, fit locally: unsupervised learning of nonlinear manifolds. J Mach Learn Res 4:119–155MATHGoogle Scholar
  30. 30.
    Song X, Peng X, Xu J, Shi G, Wu F (2015) Cloud-based distributed image coding. IEEE Trans Circuits Syst Video Technol PP(99):1–1Google Scholar
  31. 31.
    Tan TK, Boon CS, Suzuki Y (2006) Intra prediction by template matching. In: Proceedings of IEEE International Conference on Image Processing, pp 1693–1696. Atlanta, GAGoogle Scholar
  32. 32.
    Tang J, Li Z, Wang M, Zhao R (2015) Neighborhood discriminant Hashing for large-scale image retrieval. IEEE Trans Image Process 24(9):2827–2840MathSciNetCrossRefGoogle Scholar
  33. 33.
    Tao H, Huang TS (2002) Visual estimation and compression of facial motion parameters—elements of a 3D model-based video coding system. Int J Comput Vis 50(2):111–125MATHCrossRefGoogle Scholar
  34. 34.
    Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRefGoogle Scholar
  35. 35.
    Tiirkan M, Guillemot C (2012) Image prediction based on neighbor-embedding methods. IEEE Trans Image Process 21(4):1885–1898MathSciNetCrossRefGoogle Scholar
  36. 36.
    Timo D, Falko S, Wolfgang F (2011) Coding images with local features. Int J Comput Vis 94(2):154–174MATHCrossRefGoogle Scholar
  37. 37.
    Trocan M, Tramel EW, Fowler JE, Pesquet B (2014) Compressed-sensing recovery of multiview image and video sequences using signal prediction. Multimedia Tools Appl 72:95–121CrossRefGoogle Scholar
  38. 38.
    Xiaoming Z, Shiqing Z (2012) Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding. EURASIP J Adv Signal Process 2012:1–9CrossRefGoogle Scholar
  39. 39.
    Yan C, Zhang Y, Xu J, Dai F, Liang L, Dai Q, Wu F (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Sig Process Lett 21(5):573–576CrossRefGoogle Scholar
  40. 40.
    Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089CrossRefGoogle Scholar
  41. 41.
    Yeh C-H, Tseng T-Y, Lee C-W, Lin C-Y (2015) Predictive texture synthesis-based intra coding scheme for advanced video coding. IEEE Trans Multimedia 17(9):1508–1514CrossRefGoogle Scholar
  42. 42.
    Yin Z, Barner KE (2013) Locality constrained dictionary learning for nonlinear dimensionality reduction. IEEE Sig Process Lett 20(4):335–338CrossRefGoogle Scholar
  43. 43.
    Yong R (2014) Big data and image search. IEEE MultiMed 21(3):2–3CrossRefGoogle Scholar
  44. 44.
    Zhang Y, Kwong S, Xu W, Yuan H, Pan Z, Xu L (2015) Machine learning-based coding unit depth decisions for flexible complexity allocation in high efficiency video coding. IEEE Trans Image Process 24(7):2225–2238MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Physics College of Science and TechnologyYangzhou UniversityYangzhouChina

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