Lossless Data Compression Using Neural Networks
نویسندگان
چکیده
This paper deals with the predictive compression of images using neural networks (NN). The idea is to use of the backpropagation algorithm in order to compute the predicted pixels. The results validation is performed by comparison with linear prediction compression used in JPEG algorithm. Key-Words: lossless image compression, neural networks, prediction, backpropagation algorithm
منابع مشابه
Application of Generalised Regression Neural Networks in Lossless Data Compression
Neural networks are a popular technology that exploits massive parallelism and distributed storage and processing for speed and error tolerance. Most neural networks tend to rely on linear, step or sigmoidal activation functions for decision making. The generalised regression neural network (GRNN) is a radial basis network (RBN) which uses the Gaussian activation function in its processing elem...
متن کاملDeepZip: Lossless Compression using Recurrent Networks
There has been a tremendous surge in the amount of data generated. New types of data, such as Genomic data [1], 3D-360 degree VR Data, Autonomous Driving Point Cloud data are being generated. A lot of human effort is spent in analyzing the statistics of these new data formats for designing good compressors. We know from Information theory that good predictors form good Compressors [2]. We know ...
متن کاملNeural Network Based ROI Detection and Hybrid Image Compression
Region of Interest based compression is an efficient method of compression for images with a particular part to be most significant. It is always a better choice to compress the ROI with lossless compression while the rest of image with lossy compression technique. This paper proposes lossless compression for medical image(ROI) and near lossless compression for the rest of the image. Image othe...
متن کاملFast Two-Stage Lempel-Ziv Lossless Numeric Telemetry Data Compression Using a Neural Network Predictor
Lempel-Ziv (LZ) is a popular lossless data compression algorithm that produces good compression performance, but suffers from relatively slow processing speed. This paper proposes an enhanced version of the Lempel-Ziv algorithm, through incorporation of a neural pre-processor in the popular predictor-encoder implementation. It is found that in addition to the known dramatic performance increase...
متن کاملDeep feature compression for collaborative object detection
Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud. This paradigm, termed collaborative intelligence, involves communicating feature data between the mobile and the cloud. The efficiency of such approach can be further improved by lossy compr...
متن کامل