Advances in Multimedia Information Processing – PCM 2015: by Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu

By Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu

The two-volume court cases LNCS 9314 and 9315, represent the lawsuits of the 16th Pacific-Rim convention on Multimedia, PCM 2015, held in Gwangju, South Korea, in September 2015.

The overall of 138 complete and 32 brief papers awarded in those complaints was once conscientiously reviewed and chosen from 224 submissions. The papers have been geared up in topical sections named: picture and audio processing; multimedia content material research; multimedia purposes and providers; video coding and processing; multimedia illustration studying; visible figuring out and popularity on immense facts; coding and reconstruction of multimedia information with spatial-temporal info; 3D image/video processing and functions; video/image caliber overview and processing; social media computing; human motion reputation in social robotics and video surveillance; contemporary advances in image/video processing; new media illustration and transmission applied sciences for rising UHD companies.

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Extra info for Advances in Multimedia Information Processing – PCM 2015: 16th Pacific-Rim Conference on Multimedia Gwangju, South Korea, September 16–18, 2015, Proceedings, Part I

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Absolute differences are summed to create a simple metric of similarity. (b) Sum of squared differences (SSD) Rðx; yÞ ¼ X u;v ðT ðu; vÞ À Iðx þ u; y þ vÞÞ2 ð2Þ (c) Normalized sum of squared differences (NSSD) P 2 u;v ðT ðu; vÞ À I ðx þ u; y þ vÞÞ Rðx; yÞ ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2 P 2 u;v T ðu; vÞ Á u;v I ðx þ u; y þ vÞ ð3Þ SSD and NSSD work by taking the squared difference between each pixel in T and the corresponding pixel in the small parts of images being used for comparison in I.

2 Computation Cost Comparison To further compare the computation cost, we divided the entire procedure of [1] and our method into two stages: salient edge acquisition (SEA) and blind deconvolution (BD). When carrying out the previous experiment on synthesised dataset, for each image the time used in the two stages is recorded. Then we computed the average time of the total 480 trails, as shown in Table 1. Table 1. Running time comparison. The metric unit is second (s). 8070 It is easy to observe that, our method is much faster than the baseline method.

4 Kernel Refinement Since is based on the discretely sampled angular velocity, the measurement error is inevitable. The inaccurate homography may cause ringing artifacts during deconvolu‐ tion. So, we refine the initial blur kernel by additionally using a pair of long/short expo‐ sure-time images. First, we apply a proper pre-processing to the short exposure time image. Its intensity level is corrected to that of the long exposure-time image, and a specific noise reduction algorithm is applied to the intensity-corrected short exposure-time image.

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