Can You Tell a Face from a HEVC Bitstream?

Saeed Ranjbar Alvar, Hyomin Choi, I. Bajić
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引用次数: 13

Abstract

Image and video analytics are being increasingly used on a massive scale. Not only is the amount of data growing, but the complexity of the data processing pipelines is also increasing, thereby exacerbating the problem. It is becoming increasingly important to save computational resources wherever possible. We focus on one of the poster problems of visual analytics – face detection – and approach the issue of reducing the computation by asking: Is it possible to detect a face without full image reconstruction from the High Efficiency Video Coding (HEVC) bitstream? We demonstrate that this is indeed possible, with accuracy comparable to conventional face detection, by training a Convolutional Neural Network on the output of the HEVC entropy decoder.
你能从HEVC比特流中分辨人脸吗?
图像和视频分析正在越来越多地大规模使用。不仅数据量在增长,而且数据处理管道的复杂性也在增加,从而加剧了问题。尽可能节省计算资源变得越来越重要。我们专注于视觉分析的一个主要问题——人脸检测,并通过以下问题来解决减少计算的问题:是否有可能在没有从高效视频编码(HEVC)比特流中重建完整图像的情况下检测人脸?通过在HEVC熵解码器的输出上训练卷积神经网络,我们证明了这确实是可能的,其准确性与传统的人脸检测相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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