Adaptive Resolution Upconversion for Compressed Video Using Pixel Classification
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Research Article Adaptive Resolution Upconversion for Compressed Video Using Pixel Classification Ling Shao Video Processing and Analysis Group, Philips Research Laboratories, High Tech Campus 36, 5656 AE Eindhoven, The Netherlands Received 22 August 2006; Accepted 3 May 2007 Recommended by Richard R. Schultz A novel adaptive resolution upconversion algorithm that is robust to compression artifacts is proposed. This method is based on classification of local image patterns using both structure information and activity measure to explicitly distinguish pixels into content or coding artifacts. The structure information is represented by adaptive dynamic-range coding and the activity measure is the combination of local entropy and dynamic range. For each pattern class, the weighting coefficients of upscaling are optimized by a least-mean-square (LMS) training technique, which trains on the combination of the original images and the compressed downsampled versions of the original images. Experimental results show that our proposed upconversion approach outperforms other classification-based upconversion and artifact reduction techniques in concatenation. Copyright © 2007 Ling Shao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
With the continuous demand of higher picture quality, the resolution of high-end TV products is rapidly increasing. The resolution of broadcasting programs or video on storage discs is usually lower than that of high-definition (HD) TV. Therefore, those video materials have to be upconverted to fit the resolution of the HDTV. Due to the bandwidth limit of the broadcasting channels and the capacity limit of the storage media, the video materials are always compressed with various compression standards, such as MPEG1/2/4 and H.26x. These block-transform-based codecs divide the image or video frame into nonoverlapping blocks (usually with the size of 8 × 8 pixels), and apply discrete cosine transform (DCT) on them. The DCT coefficients of neighboring blocks are thus quantized independently. At high or medium compression rates, the coarse quantization will result in various noticeable coding artifacts, such as blocking, ringing, and mosquito artifacts. Most existing resolution upconversion algorithms apply content-adaptive interpolation according to the structure or property of a region [1–7]. For compressed materials, the coding artifacts will be preserved after upscaling. These coding artifacts, for example, blocking artifacts, will be even more difficult to remove than those in the original
low-resolution image, because the coding artifacts will spread among more pixels and become not trivial to detect after upscaling. One solution is to reduce the coding artifacts before applying resolution upscaling. However, most coding artifact reduction algorithms [8–11] blur details while suppressing various digital artifacts. T
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