传统的二维JND(Just Noticeable Difference)模型只能估计平面图像的最小可觉差,并不完全适用于虚拟立体视觉下的大视场角图像。首先根据人眼视觉特性,对亮度、对比度、中心凹和立体深度4种掩蔽特性设计相应的双目观测实验,通过实验数据建立JND数学模型并将其与当前其他JND模型进行对比,结果表明该模型在同等感知质量下可以去除更多的视觉冗余。然后将该视觉感知冗余模型应用于图像压缩,为此提出一种多重色阶压缩算法,该算法根据人眼色差阈值对图像的不同区域进行不同等级的色阶量化,量化过程结合Floyd-Steinberg误差分散算法可以去除视觉冗余数据。最后在FPGA(Field-Programmable Gate Array)硬件平台上完成算法的验证,结果表明该算法的平均比特压缩率可以达到61.65%,能够有效降低VR(Virtual Reality)图像所需的传输数据量。

The traditional two-dimensional JND (Just Noticeable Difference) model can only estimate the minimum noticeable difference of the planar image, and is not completely suitable for the large field of view image under virtual stereo vision. First, according to the visual characteristics of the human eye, corresponding binocular observation experiments are designed for the four masking characteristics of brightness, contrast, fovea and stereo depth, and the mathematical model of JND is established through experimental data, and compared with other current JND models. The results show that this model can remove more visual redundancy under the same perceptual quality. Then the visual perception redundancy model is applied to image compression. For this purpose, a multi-level compression algorithm is proposed. The algorithm performs different levels of color level quantization on different regions of the image according to the human eye color difference threshold. The quantization process which combines the Floyd-Steinberg error dithering algorithm can remove visual redundant data. Finally, the algorithm verification is completed on the FPGA (Field-Programmable Gate Array) hardware platform. The results show that the average bit compression rate of the algorithm can reach 61.65%, which can effectively reduce the amount of transmission data required for VR (Virtual Reality) images.