Image feature compression based on rate-accuracy optimization
In smart city,smart inspection,smart traffic and other scenes,cameras and other terminal devices will generate a large amount of image video data that will be sent into the cloud and be analyzed by intelligent processing algorithms.However,these algorithms that usually deploy a processing framework including the traditional source-side image video compression transmission,as well as the back-end feature extraction,analysis and recognition can easily cause visual feature damages and affect the analysis and recognition accuracy.Therefore,the new mechanism that extracts the image features at the source side,compresses them and transmits them to the processing framework at the back end has become a hot topic.In this paper,we propose an image feature compression method based on rate-accuracy optimization.The image features are extracted.The criteria for dividing the importance of the feature map are analyzed.And the feature map is divided into two parts:importance and non-importance features,and quantified separately.On this basis,a rate-accuracy model is established,and the optimal accuracy is solved for a given bitrate condition to determine the corresponding quantization parameters.Finally,experiments are carried out with image classification as an intelligent analysis task.The results show that the proposed method can optimize the selection of quantization parameters for different regions and obtain better coding performance.The accuracy is improved by 9.73%compared to JPEG algorithm at a low bit rate.
万方数据
维普
