A sub‐regional compression method for greenhouse images based on CNN image quality assessment

Abstract

As a major Internet of Things information carrier within the greenhouse, image quality and image file size need to be balanced. To reduce image file size while maintaining image quality, a sub-regional compression (SRC) method is proposed, and its optimal compression quality of the background regions is selected using image quality assessments such as SSIM, BRISQUE, NIQE, and PIQE. In addition, a convolutional neural network-based NIMA model to measure image quality is also being introduced. In the end, the SRC method is compared with JPEG compression default mode and JPEG compression with quality 30 in NIMA score, compression time, and compression efficiency, proving the effectiveness of the method. Practical applications Certain vegetables and fruits, such as cucumbers, tomatoes, etc., in most cases are processed directly into the hands of consumers, so the loss is directly attributable to their growth, especially pests and diseases. Monitoring for the above situation is a great improvement, but the increase in monitoring accuracy brings video/key frame to becoming larger. Therefore, it is better to reduce the file size as well as to ensure clarity in regions of interest, viewing the files faster and detecting the losses by humans or machines as easily as possible. This paper investigates the overall process and innovatively introduces deep learning into image quality assessment to better simulate human perception of images, and the research results of this paper can be applied to different models of surveillance cameras to achieve the same results.