Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/10471
Title: | Block of interest based AVS to HEVC transcoding with resolution conversion |
Authors: | Asfandyar, Malik Dr. NAWAZ Mehmood Xie, Rong Zhang, Liang Hussain, Muddsser |
Issue Date: | 2017 |
Publisher: | Springer Singapore |
Source: | Asfandyar, M., Nawaz, M., Xie, R., Zhang, L., & Hussain, M. (2017). Block of interest based AVS to HEVC transcoding with resolution conversion. In Yang, X., & Zhai, G. (Eds.). Digital TV and wireless multimedia communication: 13th international forum, IFTC 2016, Shanghai, China, November 9-10, 2016, revised selected papers. IFTC 2016, Shanghai, China (pp. 296-306). Springer Singapore. |
Conference: | 13th International Forum, IFTC 2016 |
Abstract: | Video transcoding technique is widely used in many multimedia applications. There are many transcoding techniques have been proposed in recent decade in which we map the advanced video coding standard (AVS) to high efficiency video coding (HEVC). To improve the visual efficiency of transcoding, we pro-posed a super resolution block based technique. By using this technique we improve the low resolution AVS video to high definition (HD) video. As Human eyes are more sensitive to moving objects in video contents. Where moving objects can be estimated in terms of coding by decoded motion vectors (MV), macroblock (MB) modes and transform coefficients. By using these information, proposed method classify each frame into two types of blocks, one is the most block of interest (MBOI) and second is the less block of interest (LBOI). After classification of blocks, we apply super resolution method on decoded low resolution blocks with different parameters values. The experimental results show that our method save 20% to 40% time than full frame super resolution while keeping the same visual quality in terms of PSNR and SSIM index. |
Type: | Conference Paper |
URI: | http://hdl.handle.net/20.500.11861/10471 |
ISBN: | 9789811042102 9789811042119 |
DOI: | https://doi.org/10.1007/978-981-10-4211-9_29 |
Appears in Collections: | Applied Data Science - Publication |
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