Skip to main content
SpringerLink
Log in

1D representation of locally linear embedding for image prediction

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  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):2274–2282

    Article  Google Scholar 

  2. Ankur A, Bill T (2008) Multilevel image coding with hyperfeatures. Int J Comput Vis 78(1):15–27

    Article  Google Scholar 

  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):798–811

    Article  Google Scholar 

  4. Changshui Z (2004) Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction. Pattern Recogn 37:325–336

    Article  MATH  Google Scholar 

  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 28:1202–1211

    Google Scholar 

  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):1161–1174

    Article  MathSciNet  Google Scholar 

  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):267–281

    Article  MATH  Google Scholar 

  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):487–500

    Article  Google Scholar 

  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):3574–3585

    Article  MathSciNet  Google Scholar 

  10. Farid MS, Lucenteforte M, Grangetto M (2015) Panorama view with spatiotemporal occlusion compensation for 3D video coding. IEEE Trans Image Process 24(1):205–219

    Article  MathSciNet  Google Scholar 

  11. Genaro D-S, German C-D, Principe JC (2012) Locally linear embedding based on correntropy measure for visualization and classification. Neurocomputing 80:19–30

    Article  Google Scholar 

  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):1683–1696

    Article  Google Scholar 

  13. Gudivada VN, Baeza-Yates R, Raghavan VV (2015) Big data: promises and problems. Computer 48(3):20–23

    Article  Google Scholar 

  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, Canada

  15. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  16. Hu W, Cheung G, Ortega A (2015) Intra-prediction and generalized graph Fourier transform for image coding. IEEE Sig Process Lett 22(11):1913–1917

    Article  Google Scholar 

  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.

  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):1247–1260

    Article  MathSciNet  Google Scholar 

  19. Kouropteva O, Okun O, Pietikainen M (2005) Incremental locally linear embedding. Pattern Recogn 38(10):1764–1767

    Article  MATH  Google Scholar 

  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, Poland

  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):1–1

  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, Massachusetts

  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):162–170

    Article  MathSciNet  Google Scholar 

  24. Nichols JM, Bucholtz F, Nousain B (2011) Automated, rapid classification of signals using locally linear embedding. Expert Syst Appl 38(10):13472–13474

    Article  Google Scholar 

  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, Canada

  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):1–1

  27. Roweis ST (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  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 131:31–36

    Article  Google Scholar 

  29. Saul L, Roweis S (2003) Think globally, fit locally: unsupervised learning of nonlinear manifolds. J Mach Learn Res 4:119–155

    MATH  Google Scholar 

  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):1–1

  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, GA

  32. Tang J, Li Z, Wang M, Zhao R (2015) Neighborhood discriminant Hashing for large-scale image retrieval. IEEE Trans Image Process 24(9):2827–2840

    Article  MathSciNet  Google Scholar 

  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):111–125

    Article  MATH  Google Scholar 

  34. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  35. Tiirkan M, Guillemot C (2012) Image prediction based on neighbor-embedding methods. IEEE Trans Image Process 21(4):1885–1898

    Article  MathSciNet  Google Scholar 

  36. Timo D, Falko S, Wolfgang F (2011) Coding images with local features. Int J Comput Vis 94(2):154–174

    Article  MATH  Google Scholar 

  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 72:95–121

    Article  Google Scholar 

  38. Xiaoming Z, Shiqing Z (2012) Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding. EURASIP J Adv Signal Process 2012:1–9

    Article  Google Scholar 

  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):573–576

    Article  Google Scholar 

  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):2077–2089

    Article  Google Scholar 

  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):1508–1514

    Article  Google Scholar 

  42. Yin Z, Barner KE (2013) Locality constrained dictionary learning for nonlinear dimensionality reduction. IEEE Sig Process Lett 20(4):335–338

    Article  Google Scholar 

  43. Yong R (2014) Big data and image search. IEEE MultiMed 21(3):2–3

    Article  Google Scholar 

  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):2225–2238

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Physics College of Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China

    Honggui Li

Authors
  1. Honggui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Honggui Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, H. 1D representation of locally linear embedding for image prediction. Multimed Tools Appl 76, 8651–8676 (2017). https://doi.org/10.1007/s11042-016-3491-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3491-2

Keywords

Search

Navigation