Application of optimized sparse encoding algorithm in data compression

Abstract

Data transmission is crucial in the process of equipment monitoring. The compression algorithms are adopted to reduce the amount of data transmission. When sparse encoding algorithms are used for this purpose, despite extensive attempts to improve their performance, some problems still remain. The main issues pertain to: (1) Dictionary construction − As practical dictionary better matches the input signal, such a dictionary must be efficiently established; 2) The setting of sparsity value − It is difficult to determine the size of sparsity values when prior knowledge is insufficient (when the parameter settings are too large, the cost of data transmission increases considerably, while excessively small values can result in poor reconstruction accuracy); and (3) How to implement engineering applications of data compression based on sparse encoding. To address these issues, in this work, an optimized sparse encoding algorithm is proposed by combining the non-negative Online Dictionary Learning (ODL) with the optimized Orthogonal Matching Pursuit (OMP) algorithm. First, a non-negative ODL algorithm is adopted for dictionary learning based on the training dataset to obtain an effective dictionary. Next, the optimized OMP algorithm is used to obtain the sparsity and the sparse coefficient matrix. Further analyses confirm that the reconstructed signal has a small reconstruction error. Finally, A lossy compression framework is proposed for the compressing equipment condition monitoring data using sparse encoding algorithms. Compared with the compression algorithms based on compressive sensing and DCT, the average compression ratio can reach 42.7 when the reconstruction accuracy is similar. In terms of comprehensive compression performance, the quality score is also higher compared to other algorithms.