1D representation of locally linear embedding for image prediction

作者：Li, Honggui^*

来源：Multimedia Tools and Applications, 2017, 76(6): 8651-8676.

DOI：10.1007/s11042-016-3491-2

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

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.

• 出版日期2017-3
• 单位扬州大学