Adaptive Radiographic Image Compression Technique using Hierarchical Vector Quantization and Huffman Encoding

Abstract

The objective of this paper is to provide a proposed method for radiographic image compression with maximum Compression Ratio (CR) as possible and keeping all details, especially in the Region Of Interest (ROI) that contains the important information in the image. In the proposed method; firstly, the ROI is separated from the image background using an automatic threshold based on the occurrence histogram of the variance image, then the image background is compressed with the maximum possible compression ratio using image pyramid compression followed by lossy Vector Quantization (VQ) compression technique based on Generalized Lloyd Algorithm (GLA) method for generating the codebook. After that, ROI is compressed using the Huffman Code (HC) with low compression ratio and with minimum loss in details. Finally, the compressed image is obtained by combing both the compressed background and the compressed ROI. The results are evaluated by calculating the Normalized Cross Correlation (NCC) and the Structural Similarity Index (SSIM) between the original image and the recovered image after decompression at different compression ratios. The obtained results are compared with the results obtained from compressing the whole image without separation using lossy VQ, HC, Discrete Cosine Transform (DCT), and JPEG2000 compression methods. The results show that, the proposed method owns more reliable performance than completely compressing the radiographic image without separation using VQ compression or Huffman coding compression.