GA-based DCT quantisation table design procedure for medical images

Medical images are widely used in the diagnosis of diseases. These imaging modalities include computerised tomography (CT), magnetic resonance imaging (MRI), ultrasonic (US) imaging, X-radiographs, etc. However, medical images have large storage requirements when high resolution is demanded; therefore, they need to be compressed to reduce the data size so as to achieve a low bit rate for transmission or storage, while maintaining image information. The Joint Photographic Experts Group (JPEG) developed an image compression tool that is one of the most widely used products for image compression. One of the factors influencing the performance of JPEG compression is the quantisation table. The bit rate and the decoded quality are determined simultaneously by the quantisation table, and therefore, the table has a strong influence on the whole compression performance. The author aims to provide a design procedure to seek sets of better quantisation parameters to raise the compression performance to achieve a lower bit rate while preserving high decoded quality. A genetic algorithm (GA) was employed to promote higher compression performance for medical images. The goal was to develop a design procedure to find quantisation tables that contribute to better compression efficiency in terms of bit rate and decoded quality. Simulations were carried out on different kinds of medical images. Resulting experimental data demonstrate that the GA-based search procedures can generate better performance than JPEG 2000 and JPEG even though the training images have different features. Additionally, if existing published quantisation tables are put into the crossover pool in the proposed GA-based system, it can improve the performance by yielding better quantisation tables.