Abstract
Image compression algorithm of floral canopy based on mask hybrid coding for ROI
Highlights
Plant growth information forms an important decisionmaking basis for modern intelligent facilities and horticulture control systems to achieve precise control[1]
SPIHT encoding is applied for the Region of interest (ROI) to enable the priority coding of the ROI, whereas embedded zerotree wavelet (EZW) coding is adopted for the BG; the reason is to acquire relatively many low-frequency coefficients while ensuring a relatively small proportion of coding, making the BG reconstructed image able to reflect the global status of the BG; according to the compression ratio BPP and the coding proportional coefficient k (k is the proportion of ROI image coding, and 1-k is the proportion of BG image coding; k is calculated from the ROI proportion a and the weighing factor r of the ROI coding amount, and the range of k values is 0-1), the coding of the ROI and BG are combined into the bit streams for information transmission or storage
The results indicate that in the range of image compression rate from 0.07 bpp to 1.09 bpp (r=4), the peak-signal-to-noise ratio (PSNR) of the ROI reconstructed image is 42.65% higher on average than that of the BG, 43.95% higher on average than that of the ROI-MHC reconstructed image, and 16.84% higher on average than that of the SPIHT reconstructed image, suggesting that the ROI-MHC image compression algorithm can improve the quality of ROI image reconstruction and that its performance is superior to the standard SPIHT algorithm
Summary
Plant growth information forms an important decisionmaking basis for modern intelligent facilities and horticulture control systems to achieve precise control[1]. These settings require the collection of a large amount of plant images. The systems with limited bandwidth or capacity need to perform high-quality image compression. Region of interest (ROI) coding can be used to address the tradeoff between image quality and compression ratio in the application of image compression and achieve local ROI reconstruction with high resolution under the condition of low bit rate transmission[2,3]. ROI coding has a broad scope of applications in detection using unmanned aerial vehicles[4], medical imaging[5], and wireless image transmission with limited bandwidth[2,3], among others
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