In the recent past, the image compression plays vital role for efficient wireless communication and storage of images and videos in the field of communication engineering, satellite communication and biomedical applications. The image compression methodology is the important task for high capacity wireless transmission and real time processing for Internet of Things (IoT). The main objective of image compression is to estimate the innovative representation in that pixels are less correlated and should maintain the same contents and quality should remain same. In the proposed system the modified SPIHT (set-partitioning in hierarchical trees) is proposed with Huffman coding for lossless image compression. The low complexity 2 dimensional discrete wavelet transform (2D DWT) is used to transform the spatial domain image into frequency domain for image compression. The frequency domain encoding methodology is proposed in CMOS image sensor in that the brightest pixels components within each block fires first and that is chosen as the reference pixels for compression. The various gray scale values between subsequent pixels and the reference pixels within each block are estimated and that is applied for quantization based on the reduced number of bits for its dynamic range of compression for lossless to maintain the quality of the image. The experimental results prove that the proposed system is used to improve the Peak Signal to Noise Ratio (PSNR) and negligible Mean Square Error (MSE) to improve the efficiency and accuracy with high quality of image compression.
Volume 11 | 02-Special Issue
Pages: 298-307