DocumentCode :
1799566
Title :
A class-specified learning based super resolution for low-bit-rate compressed images
Author :
Han Zhao ; Xiaoguang Li ; Li Zhuo
Author_Institution :
Signal & Inf. Process. Lab., Beijing Univ. of Technol., Beijing, China
fYear :
2014
fDate :
14-18 July 2014
Firstpage :
1
Lastpage :
6
Abstract :
Due to limitations on the image capturing devices, distance, storage capability and bandwidth for transmission, many images in multimedia applications are low-bit-rate compressed and low resolution. In this paper, we proposed a class-specified learning based super resolution for this kind of low quality images. Firstly, we proposed a class-specified filter to remove the compressed distortions. Then a class-specified learning based scheme is employed to super resolve images with different compression rates. Experimental results show that the proposed method can improve both the objective and subjective quality of the images effectively.
Keywords :
image coding; image resolution; learning (artificial intelligence); class-specified learning based super resolution; image capturing device; image compression; image quality; image resolution; low-bit-rate compressed image; multimedia applications; Image coding; Image resolution; Information filters; Predictive models; Training; Visualization; Class-specified learning; Image super resolution; Low-bit-rate compression;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo Workshops (ICMEW), 2014 IEEE International Conference on
Conference_Location :
Chengdu
ISSN :
1945-7871
Type :
conf
DOI :
10.1109/ICMEW.2014.6890649
Filename :
6890649
Link To Document :
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