Local, Non-local and Global Predictions based Image Compression and Image Restoration
Date Issued
2014
Date
2014
Author(s)
Chen, Hsin-Hui
Abstract
With the advancement of multimedia technologies, the huge amount of data such as images and videos, etc., needs to be compressed for storage and transmission. There-fore, data compression is a very important issue. Although many different methods have been proposed, it is believed that there is still room for improvement.
On the other hand, image restoration has been an active research topic in computer vision and image processing. In conventional filtering methods, the local neighboring image data is usually used for weight optimization. It limits the overall performance due to ignoring the repetitive structure patterns in images. Since the work of the non-local means (NLM) algorithm, many more powerful methods for image denoising, interpola-tion and deblurring are proposed in recent years based on the non-local principle, which exploits the non-local self-similarity between the patches in an image.
Adaptive prediction is a key component in lossy/lossless image compression and image restoration. To remove the spatial redundancy in images, the adaptive prediction methods for generalizing the zigzag scanning method, obtaining the better coding pa-rameter for shorter codelength in image compression, interpolating missing pixels or filtering out noise in image restoration are developed.
In this dissertation, I adopt the idea of local, non-local, and global predictions to improve lossless and lossy image compression methods and image restoration methods, such as image denoising and image interpolation. In sum, there are five individual re-search works. For lossy image coding, two research works are presented. One is the proposed joint-probability-based adaptive Golomb coding (JPBAGC) algorithm, which takes the local neighboring data into account to adaptively adjust the Golomb parameter for yielding shorter codelength. The other one is the local- and global-prediction-based adaptive scanning (LGPAS), which generalizes the zigzag scanning method. It is pro-posed to achieve a better compression performance using the local and global infor-mation from previously encoded/decoded blocks in DCT-based image coding systems. For lossless image coding, non-local context modeling and adaptive prediction (NCMAP) are proposed to reduce the image prediction errors and estimate their true probability distribution by exploiting the non-local structural self-similarity in the spatial and prediction error domain.
For image denoising, a powerful and efficient scheme, called non-local means based on bidirectional principal component analysis (NLM-BDPCA), is proposed. Fur-thermore, the coarse-to-fine algorithm is also implemented. It does not perform de-noising iteratively and can well preserve the edge/texture information. For image inter-polation, the structural similarity based metric is incorporated into the framework of non-local edge-directed image interpolation (SSNLEDI) for yielding better peak-to-signal ratio (PSNR) values. Overall, these five efforts take the advantage of the rich information extracted from local, non-local, or global regions in images to achieve better performance for image compression and image restoration.
Subjects
影像壓縮
霍夫曼編碼法
格倫布編碼法
可適性格倫布編碼法
鋸齒狀掃描
可適性係數掃描法
H.264/AVC
畫面內預測法
邊緣導向預測法
非區域性平均法
結構自相似性
雙向主成分分析法
影像去雜訊
影像內插
Type
thesis
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