Region-of-interest-based video transcoding from MPEG-2 to H.264 in the compressed domain

2008 ◽  
Vol 47 (9) ◽  
pp. 097001 ◽  
Author(s):  
Rong Xie
Keyword(s):  
Region Of Interest ◽  
Video Transcoding ◽  
Compressed Domain

Content-based video transcoding in compressed domain

2002 ◽  
Vol 17 (6) ◽  
pp. 497-507 ◽  
Author(s):  
TaeYong Kim ◽  
Jong Soo Choi
Keyword(s):  
Video Transcoding ◽  
Compressed Domain

scholarly journals Compressed-Domain Techniques for Error-Resilient Video Transcoding Using RPS

2009 ◽  
Vol 18 (2) ◽  
pp. 357-370 ◽  
Author(s):  
Yui-Lam Chan ◽  
Hoi-Kin Cheung ◽  
Wan-Chi Siu
Keyword(s):  
Video Transcoding ◽  
Compressed Domain ◽  
Error Resilient

scholarly journals Deep Learning-based Histopathological Segmentation for Whole Slide Images of Colorectal Cancer in a Compressed Domain

2021 ◽  
Author(s):  
Hyeongsub Kim ◽  
Hongjoon Yoon ◽  
Nishant Thakur ◽  
Gyoyeon Hwang ◽  
Eun Jung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wavelet Transform ◽  
High Resolution ◽  
Discrete Wavelet Transform ◽  
Region Of Interest ◽  
Principal Component ◽  
Spatial Domain ◽  
Discrete Wavelet ◽  
Compressed Domain ◽  
Segmentation Approach

Abstract Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score was 0.852 ± 0.086 and the pixel accuracy was 0.962 ± 0.027. We can train the networks for the high-resolution image (magnification x20) compared to the result in the spatial domain (magnification x10) in same the region of interest (6.25 × 10^2 um^2). The average Dice score and pixel accuracy are significantly increased by 6.4 % and 1.6 %, respectively. We believe that our approach has great potential for accurate diagnosis in pathology.

scholarly journals Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyeongsub Kim ◽  
Hongjoon Yoon ◽  
Nishant Thakur ◽  
Gyoyeon Hwang ◽  
Eun Jung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wavelet Transform ◽  
High Resolution ◽  
Discrete Wavelet Transform ◽  
Region Of Interest ◽  
Principal Component ◽  
Small Region ◽  
Spatial Domain ◽  
Discrete Wavelet ◽  
Compressed Domain

AbstractAutomatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.

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