An adaptive image compression algorithm based on joint clustering algorithm and deep learning

In recent years, deep artificial neural networks have attracted much attention and have been applied in various fields because they surpass the parameter fitting effect of traditional methods under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity make image compression necessary in communication. Here, a compression algorithm that combines the K-means clustering algorithm with the neural network algorithm is proposed. First, the pixel points of the image are clustered by K-means algorithm in order to reduce the amount of data input to the neural network algorithm. Secondly, neural network is used to extract image features which realizes further compression. The experiment results show that the peak signal-to-noise ratio (PSNR) is 33.48 dB at most with compression ratio at 32:1. The ablation experiment shows that the run time speeds up 9.5% compared to the algorithm without K-means clustering. Comprehensive comparison experiment shows that the average PSNR is 30.09 dB, which is larger than other baseline approaches. The proposed algorithm is an efficient solution for image compression. A compression algorithm combining K-means algorithm with neural network algorithm is proposed. Simulation results show that this proposed algorithm is superior to other baseline algorithms in terms of peak signal-to-noise ratio value and subjective quality.image