3D Microscopic image coding by finite-state vector quantization in an enhanced image pyramid

A novel approach, based on a 3D difference pyramid structure with vector quantization error feedback, is proposed for microscopic volume image data compression. We have improved the coding performance relative to previous work. A finite-state vector quantizer (FSVQ) is introduced to exploit the correlation between neighbouring vectors to improve the coding efficiency. The effects of FSVQ in conjunction with thresholding are investigated. A distortion minimization algorithm selects both the setting of thresholds and size of state codebook. Experiments have been performed on data sets obtained by confocal laser scanning microscopy (CLSM) scans of human arteries. Results demonstrate that our new coding technique substantially improves the subjective and objective quality of the decompressed images over Moving Picture Expert Group (MPEG)-1 with more than 5dB gain. Compared to the state-of-the-art 3D volume coder 3D-Set Partition in Hierarchical Trees (SPIHT), our method also offers better coding performance with roughly 0.1 dB higher at high rate and more than 0.6 dB higher at very low bit rate.