Importance Sampling for Evaluation of Video Transcoder Performance

Optimization of a video transcoder is performed, e.g., by fine-tuning their parameters, based on evaluation of the performance of a transcoder over a small, fixed video dataset. The use of a small, fixed video dataset enables reproducibility, fast evaluation, and regression testing. However, transcoders that are fine-tuned based on a small, fixed dataset can often deliver suboptimal transcoding performance when utilized to transcode videos from a much larger dataset, e.g., videos served by a video hosting and sharing service. This is because a small, fixed set of videos is not sufficiently representative of the total corpus of videos hosted by a video sharing service and does not cover the scale and diversity of such videos. This disclosure describes the use of importance sampling in the evaluation of video transcoders using a small dataset of videos. The techniques can deliver a high-performance transcoder even when the transcoder is optimized using a small dataset that is insufficiently representative of a large-scale video corpus

Deformable block based motion estimation in omnidirectional image sequences

This paper presents an extension of block-based motion estimation for omnidirectional videos, based on a camera and translational object motion model that accounts for the spherical geometry of the imaging system. We use this model to design a new algorithm to perform block matching in sequences of panoramic frames that are the result of the equirectangular projection. Experimental results demonstrate that significant gains can be achieved with respect to the classical exhaustive block matching algorithm (EBMA) in terms of accuracy of motion prediction. In particular, average quality improvements up to approximately 6dB in terms of Peak Signal to Noise Ratio (PSNR), 0.043 in terms of Structural SIMilarity index (SSIM), and 2dB in terms of spherical PSNR, can be achieved on the predicted frames