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.

与文字和声音相比，图像在水下数据收集和传输研究中起着不可替代的作用。但是，它遭受了水下声通信的有限带宽的负担，无法提供大的图像数据。在水下图像通信中，在传输之前压缩图像数据是不可避免的过程。与往常一样，自然图像压缩方法直接应用于水下场景。众所周知，由于光的透射特性，水下图像与自然图像的降解程度不同。水下的低照度会导致比空气中更严重的模糊和褪色。降低水下图像的比特率，同时尽可能保留压缩图像质量是一个巨大的挑战。在本文中，在水下图像通信的压缩和评估阶段，要考虑人类视觉系统（HVS）。我们提出了一种用于水下图像压缩的新方法。首先，通过考虑人的视觉系统，提出了色度感知算子，以忽略在水下成像中普遍存在的色度不易察觉的位移，从而提高了图像的压缩率。其次，水下图像的景深通常较浅，并且大多数可用图像中都有目标。然后将基于布尔图检测的ROI提取算法用于水下图像压缩，以减少压缩图像的比特率。此外，水下图像颗粒状且对比度低，这意味着降级发生在图像的某些区域，不会被察觉。在图像处理中还考虑了基于HVS的空间和频域掩蔽特征的Just Notice Difference（JND）感知算法。通过结合以上三个方面，混合小波和非对称编码一起用于促进水下图像压缩，从而使图像具有更好的质量和更少的冗余。实验表明，该方法可以充分利用水下图像的固有特性，在不降低重建图像视觉感知质量的前提下，最大化水下图像的视觉冗余度。在图像处理中还考虑了基于HVS的空间和频域掩蔽特征的Just Notice Difference（JND）感知算法。通过结合以上三个方面，混合小波和非对称编码一起用于促进水下图像压缩，从而使图像具有更好的质量和更少的冗余。实验表明，该方法可以充分利用水下图像的固有特性，在不降低重建图像视觉感知质量的前提下，最大化水下图像的视觉冗余度。在图像处理中还考虑了基于HVS的空间和频域掩蔽特征的Just Notice Difference（JND）感知算法。通过结合以上三个方面，混合小波和非对称编码一起用于促进水下图像压缩，从而使图像具有更好的质量和更少的冗余。实验表明，该方法可以充分利用水下图像的固有特性，在不降低重建图像视觉感知质量的前提下，最大化水下图像的视觉冗余度。