Improving Reconstructed Image Quality Via Hybrid Compression Techniques

Abstract

Data compression is one of the core fields of study for applications of image and video processing. The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result, it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques, the data must be reconstituted very similarly to the initial form. In this paper, a hybrid compression based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) is used to enhance the quality of the reconstructed image. These techniques are followed by entropy encoding such as Huffman coding to give additional compression. Huffman coding is optimal prefix code because of its implementation is more simple, faster, and easier than other codes. It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). We applied a hybrid algorithm on (DWT–DCT 2 × 2, 4 × 4, 8 × 8, 16 × 16, 32 × 32) blocks. Finally, we show that by using a hybrid (DWT–DCT) compression technique, the PSNR is reconstructed for the image by using the proposed hybrid algorithm (DWT–DCT 8 × 8 block) is quite high than DCT.