Low bit-rate compression of underwater image based on human visual system

Abstract

Image plays an irreplaceable role compared with the text and sound in the underwater data collection and transmission researches. However, it suffers from the limited bandwidth of the underwater acoustic communication which cannot afford the large image data. Compressing the image data before transmission is an inevitable process in the underwater image communication. As usual, the natural image compression methods are directly applied to the underwater scene. As we all know, underwater image has different degradation from the natural one due to the optical transmission property. Low illumination in underwater will cause more seriously blurring and color fading than that in the air. It is a great challenge to decrease the bit-rate of the underwater image while preserving the compressed image quality as much as possible. In this paper, the Human Visual System (HVS) is taken into account during the compressing and the evaluating stages for the underwater image communication. We present a new methodology for underwater image compression. Firstly, by taking the human visual system into account, the chrominance perception operator is proposed in this paper to neglect the imperceptible chrominance shift which is widely exited in the underwater imaging to improve the image compression rate. Secondly, depth of field(DOF) of underwater image is usually shallow and most of the usable image has targets in it. An ROI extraction algorithm based on Boolean map detection is then used for the underwater image compression so as to reduce the bitrate of the compressed image. Furthermore, the underwater image is grainy and low contrast, that means the degradation happens in some regions of the image would not be perceived. Just notice difference(JND) sensing algorithm based on the spatial and frequency domain masking feature of HVS is also considered in the image processing. By combining the three aspects above, hybrid wavelet and asymmetric coding are used together to promote the underwater image compression, so that the image can have better quality and less redundancy. Experiments show that the proposed method can make full use of the inherent characteristics of underwater images, and maximize the visual redundancy of underwater images without reducing the visual perception quality of reconstructed images.