Dynamically Adaptive Fast Motion Estimation Algorithm for HD Video
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Dynamically Adaptive Fast Motion Estimation Algorithm for HD Video Pavel Arnaudov 1 & Tokunbo Ogunfunmi 1 Received: 25 April 2020 / Revised: 25 April 2020 / Accepted: 22 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper presents a novel adaptive Fast Motion Estimation (FME) Algorithm, which reduces the number of search points, computational complexity and therefore lowering power consumption, while providing improved quality per watt in FME. The algorithm minimizes computation and storage and from there, power consumption, while trying to preserve quality as much as possible. It is very suitable for hardware implementation, as in a motion co-processor. The pervasiveness of mobile devices with HD video capabilities demands such low power hardware accelerators. The proposed algorithm is a novel method, that can identify the best search pattern within a given region of the HD video frame, based on its motion dynamics to achieve lower power video encoding. The goal is to achieve the best quality with the minimum number of search iterations. The reduced number of checks translates into power savings. The motivation of the presented algorithm is to learn the motion dynamics within a region from a small subset of blocks within that region and apply that knowledge to the remaining large set of blocks in the same region. This adaptiveness makes it robust with respect to the nature of the video. Thus it works equally well with more dynamic and with less dynamic video sequences. The results show that the proposed algorithm can reduce computations by about 4 times compared to other fixed search patterns algorithms, while staying within 1 dB of PSNR results. That equates to about 75% power savings at the expense of not more than 1 dB of PSNR quality loss. Keywords Motion estimation . H.264 . HEVC . AI . ML . ASIC . VLSI . HMDS . Adaptive . Video coding . Video compression . Video/multidimensional signal processing
1 Introduction Block matching Motion Estimation (ME) algorithms in general, attempt to find similar blocks of pixels in different frames, so that instead of storing/transmitting all the values of the pixels in a given block, only the differences can be stored/transmitted plus the pointer referring to the most similar block in another frame, thus reducing bandwidth and power. Motion Estimation (ME) is the most computational time and power intensive operation performed during video encoding. HD frames (Full HD 1920 × 1080) can be Featured Application: Video Compression: Applicable to both HEVC and H.264 video standards. * Pavel Arnaudov [email protected] * Tokunbo Ogunfunmi [email protected] 1
Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA
considered matrices of size 1080 × 1920. They are split into smaller parts (blocks of sizes 16 × 16, 32 × 32, 64 × 64, 8 × 8, etc.) which are effectively convolved in various ways with other blocks of the same size. Nowadays this HD video encoding operation is mainly done on cell phones and/or battery-opera
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