Perceptual video coding method based on JND and AR model

基于JND和AR模型的感知视频编码方法(英文)

In order to achieve better perceptual coding quality while using fewer bits, a novel perceptual video coding method based on the just-noticeable-distortion （JND） model and the auto-regressive （AR） model is explored. First, a new texture segmentation method exploiting the JND profile is devised to detect and classify texture regions in video scenes. In this step, a spatial-temporal JND model is proposed and the JND energy of every micro-block unit is computed and compared with the threshold. Secondly, in order to effectively remove temporal redundancies while preserving high visual quality, an AR model is applied to synthesize the texture regions. All the parameters of the AR model are obtained by the least-squares method and each pixel in the texture region is generated as a linear combination of pixels taken from the closest forward and backward reference frames. Finally, the proposed method is compared with the H.264/AVC video coding system to demonstrate the performance. Various sequences with different types of texture regions are used in the experiment and the results show that the proposed method can reduce the bit-rate by 15% to 58% while maintaining good perceptual quality.

为了达到减少比特数同时保持画面质量的目的,提出了一种基于最小可视失真(JND)和自回归(AR)模型的感知视频编码方法.首先,设计了基于JND的纹理分割算法,建立了空时JND模型,以MB为基本单元,通过计算其JND能量并与阈值做比较,用以分割出视频序列中的纹理区域.然后,开发了AR模型来合成纹理区,在使用最小二乘法计算出AR模型的参数后,用相邻的前后参考帧对应像素的线性插值来生成重构像素.最后,为了检验所提方法的效果,将其与H.264/AVC视频编码系统做比较,用不同的视频序列实验来验证所提方法的有效性.实验结果显示,对于具有不同纹理特点的实验序列,所提方法可以在保持感知质量的同时将比特率减少15%～58%.