SSIM based image quality assessment for vector quantization based lossy image compression using LZW coding

The recent development in the digital electronics and computer engineering has resulted in generation of large amount of data in the digital form. High resolution images are required in many fields such as remote sensing, criminal investigation, medical imaging etc. This motivates the need of compression of the size of the data. The main objective of the present study is to obtain better quality of decompressed images even at very low bit rates and to reduce the size of the data as well as processing and transmission time. Considerable efforts have been made to design image compression methods. There are various lossy image compression techniques existing in digital domain, among them vector quantization (VQ) based image compression technique provides good picture quality and higher compression ratio. Vector quantization is an effective technique for still image compression which gives higher quality of reconstructed image at low bit rate .This paper presents a lossy image compression technique which combines Vector Quantization (VQ) and LZW (LempelZiv – Welch) coding. In the proposed method, image is vector quantized and later VQ indices are coded using LZW coding to increase the compression ratio but, the reduction of processing time is the major issue in VQ. Experimental Patel A. and Shah D/ University Journal of Research Vol. 01, Issue 01 (2015) ISSN: 0000–0000 17 17 results are measured in terms of Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index Measurement (SSIM) for image quality The proposed method of lossy image compression shows better image quality in terms of PSNR at the same compression ratio as compared to other VQ based image compression techniques.