Surveillance and security scenarios usually require high efficient facial image compression scheme for face recognition and identification. While either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose a Learning based Facial Image Compression (LFIC) framework with a novel Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric, including a successfully exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework's efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% bitrate saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate LFIC's superior performance gain compared with latest specific facial image codecs. Visual experiments also show some interesting insight on how LFIC can automatically capture the information in critical areas based on semantic distortion metrics for optimized compression, which is quite different from the heuristic way of optimization in traditional image compression algorithms.
翻译:虽然传统的普通图像编码或特殊面部图像压缩办法只能按照面对面的核查准确度标准对代码进行精细化。我们提议了一个基于学习的面部图像压缩(LIFC)框架,并配有一个全新的区域适应性集合(RAP)模块,其参数可根据综合混合语义忠诚度指标的梯度反馈自动优化,包括成功地探索如何将Genemental Aversarial网络(GAN)直接用作图像压缩办法的衡量标准。实验结果通过显示71.41%、48.28%和52.67%的性能改进来验证框架的效率,在JPEG2000、WebP和基于神经网络的代码下分别实现节约。我们还根据最新的特定面部图像编码值评估LIC的优异性业绩收益。视觉实验还显示了一些有趣的了解,即LICIC如何能够根据优化压缩的语义扭曲度指标自动获取关键领域的信息,这与传统图像压缩压压压的超常化方法截然不同。