Performance Improvement of Lossy Image Compression Based on Polynomial Curve Fitting and Vector Quantization Shaimaa Othman , Amr Mohamed , Abdelatief Abouali , and Zaki Nossair Abstract Lossy image compression performs a fundamental role in modern commu- nication technology to cope up with the transmission and storage problems. In this paper, we present a new efficient lossy image compression method based on the polynomial curve fitting approximation technique, which represents many pixels of the image by a minimum number of polynomial coefficients. The presented method starts by converting the image into one-dimensional signal and it divides this one-dimensional signal into segments of variable length. Then, the polynomial curve fitting is applied to these segments to construct the coefficients matrix. The number of pixels is selected depending on the Root Mean Squared Error threshold value. Finally, the coefficients matrix is quantized using the vector quantization which composed of three procedures: codebook design procedure, encoding proce- dure, and decoding procedure. The proposed method is implemented for gray and colored images. Experimentally, the proposed method has improved the reconstruc- tion quality by 2–9 dB with a better compression ratio relative to other techniques. Also, the proposed method obtains a better result than any other compared algorithms. Keywords Lossy compression · Polynomial curve fitting · Vector quantization · Encoding · Decoding 1 Introduction Image compression has a great interest in reducing the size of the image without affecting the quality of it. The main target of image compression is improving the compression ratio to facilitate the resource sharing and data storage, which serve the communication process. It decreases the size of the image, so that the compressed image could be stored in less number of bits on the storage device and sent through S. Othman (B) · A. Mohamed · Z. Nossair Faculty of Engineering, Helwan University, 11795 Cairo, Egypt S. Othman · A. Abouali Faculty of Computer Science, El-Shorouk Academy, 11837 Cairo, Egypt © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. S. Kaiser et al. (eds.), Information and Communication Technology for Competitive Strategies (ICTCS 2020), Lecture Notes in Networks and Systems 190, https://doi.org/10.1007/978-981-16-0882-7_25 297