Image Compression:A Systematic Review and Evaluation

In the present scenario, storage, transmission and faster computation are the basic needs of image processing world. Image compression plays an important role in delivering these three features to image processing applications. Huge variety of algorithms and techniques are available for lossless and lossy image compression. This paper presents a systematic literature review of image compression techniques presenting the basic concepts and available methods with their research gaps. An approach is proposed by reinforcing ROI based compression method based on separation of ROI and Non-ROI parts of the image where Huffman Encoding is applied to compress ROI part and Quad Tree Decomposition is applied for Non-ROI part. Performance evaluation of studied image compression techniques is done using parameters like Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP). Experimental results of performance evaluation demonstrate that proposed technique outperforms other techniques.