Copyright © 2018 Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (2.22) (2018) 9-14 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research paper Effective lossy and lossless color image compression with Multilayer Perceptron Dr. PL. Chithra *, A. Christoper Tamilmathi Department of Computer science, University of Madras, Chennai, India *Email: chitra.cs@unom.ac.in Abstract This paper presents the effective lossy and lossless color image compression algorithm with Multilayer perceptron. The parallel struc- ture of neural network and the concept of image compression combined to yield a better reconstructed image with constant bit rate and less computation complexity. Original color image component has been divided into 8x8 blocks. The discrete cosine transform (DCT) applied on each block for lossy compression or discrete wavelet transform (DWT) applied for lossless image compression. The output coefficient values have been normalized by using mod function. These normalized vectors have been passed to Multilayer Perceptron (MLP). This proposed method implements the Back propagation neural network (BPNN) which is suitable for compression process with less convergence time. Performance of the proposed compression work is evaluated based on three ways. First one compared the performance of lossy and lossless compression with BPNN. Second one, evaluated based on different sized hidden layers and proved that increased neurons in hidden layer has been preserved the brightness of an image. Third, the evaluation based on three different types of activation function and the result shows that each function has its own merit. Proposed algorithm has been competed with existing JPEG color compression algorithm based on PSNR measurement. Resultant value denotes that the proposed method well performed to produce the better reconstructed image with PSNR value approximately increased by 21.62%. Keywords: Activation function; Back propagation neural network; Discrete cosine transform; Error; Hidden layer; JPEG compression. 1. Introduction Nowadays communication medium rapidly transformed with image transformation. So the image compression has taken the essential part to reduce not only the storage and band width of the transmis- sion and also the time and cost. Image compression has been done in two ways, lossy and lossless compression. JPEG compression method is the one of the most significant lossy compression algo- rithm. Another one is DWT for lossless image compression. This proposed work deals with both JPEG and DWT compression com- bined with multilayer perceptron. BPNN is one of the Multilayer perceptron algorithms. Combination of BPNN and JPEG compres- sion algorithm eliminates the need of quantization table and entropy symbol table and produces the constant bit rate compression. The Training algorithm and back propagation neural network is used to increase the performance and to decrease the convergence time and provide high compression ratio with low distortion [1]. Effectively compress a wide range of novel images using back propagation. The networks operate and are trained on residual image blocks [2]. Haar wavelet transform and discrete cosine transform are consid- ered and a neural network is trained to relate the x-ray image which controls compression method and their optimum compression ratio [3]. Back propagation neural network algorithm helps to increase the performance of the system and to decrease the convergence time for the training of the neural network [4]. Fast BP results such as compression ratio (CR) and peak signal to noise ratio (PSNR) are computed and compared with BP results. From the results, we no- ticed that the use of FBP improve the BPNN training by reducing the convergence time of image compression learning process [5]. Neural network is well suited for real time systems because of their fast response and computational times which are due to their paral- lel architecture [6]. If a network gets trained up successfully with a particular activation function, then there is a high probability that other activation function will also lead to proper training of neural network [7]. The concept of image compression using ANN and DWT possess the advantages of simple computations, fault tolerance, parallel pro- cessing, robust with respect to error transmission in the communi- cation media which has made a breakthrough in supervised learning of layered neural network [8]. Keeping the quality of the image con- siderably same even after transmitting via noisy channel in their BPNN algorithm. Thus the data transmission can be done with less band width, power and storage space [9]. JPEG image compression using FPGA with artificial neural networks gives more compres- sion ratio when compared to existing systems [10]. The implement of ANN with CSD based multiplier is thus proven to be one of the effective processes for image compression and decompression [11]. Steps of back propagation algorithms and manual calculations have been displayed [12]. In image compression technique convergence time also play main role for quality of image and the back propaga- tion neural network and Levengberg - marqurdt algorithms estimat- ing a connection counted in which image compression and conver- gence time have been improved [13]. The bipolar technique is pro- posed and implemented for image compression and obtained the better results as compared to principal component analysis (PCA technique). It is observed that the Bipolar & LM algorithm suits the