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Detection and compression of moving objects based on new panoramic image modeling

机译:基于新的全景图像建模的运动对象检测和压缩

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摘要

In this paper, a parametric video coding method based on new panoramic modeling is proposed for panning cameras. An input video frame from a panning camera is decomposed into a background image, rectangular moving object regions, and a residual image. Each area is then coded separately. In coding the background, we employ a panoramic model that can account for several image formation processes, such as perspective projection, lens distortion, vignetting and illumination effects. Moving objects are detected, and their minimum bounding rectangular regions are coded using a JPEG-2000 coder. The reconstruction error using only the estimated background and the moving objects is computed, and the residual image is separately encoded for image quality enhancement and rate control. We evaluated the effectiveness of the proposed algorithm using several indoor and outdoor sequences and found that the peak signal-to-noise ratio (PSNR) improved by 1.3~4.4 dB compared to that of JPEG-2000.
机译:提出了一种基于全景模型的参数化视频编码方法。来自摇摄相机的输入视频帧被分解为背景图像,矩形移动物体区域和残留图像。然后将每个区域分别编码。在对背景进行编码时,我们采用了全景模型,该模型可以说明几种图像形成过程,例如透视投影,镜头变形,渐晕和照明效果。检测到移动物体,并使用JPEG-2000编码器对它们的最小边界矩形区域进行编码。计算仅使用估计的背景和运动对象的重建误差,并且对残差图像进行单独编码以进行图像质量增强和速率控制。通过对几种室内和室外序列算法的有效性进行评估,发现峰值信噪比(PSNR)比JPEG-2000提高了1.3〜4.4 dB。

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