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An Optimized Method for B-Mode Echocardiographic Video Compression Based on Motion Estimation and Wavelet

机译:基于运动估计和小波的B模式超声心动图视频压缩优化方法

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摘要

In this paper, a new approach for echocardiography image compression is developed. To achieve a high rate of image compression as well as preserving the image information, motion detection and wavelet transform are combined. In the first step, a Region Of Interest (ROI) is determined and the image is divided into several (8x8 pixels) blocks. Thereafter, the motion vectors of each block are estimated to predict the subsequent frame (predicted model frame). Additionally, the wavelet component is created by applying the wavelet transform to the main image (which should be predicted); whereas the extracted wavelet component is utilized as a predicted frame error compensator. Subsequently, entropy of the motion vectors of each block is extracted as a criterion to determine the level of quantization which is used for wavelet frame quantization. Wavelet frame is quantized based on the Lloyd's algorithm. Finally, the correlation of each block of the predicted model frame with the corresponding block of the main image is calculated to evaluate the rate of the accuracy of the result. If the calculated correlation is more than 0.5, an optimized combination of the predicted model and the corresponding block of the wavelet frame is utilized as the final block. Otherwise, the block of the wavelet frame is considered as the final result. The results were analyzed using PSNR, MSE, GLCM and expert-based quality image validation. The proposed algorithm was compared to standard MPEG standards, H.264 and VC1 which proved the out-performance of the proposed algorithm.
机译:本文提出了一种超声心动图图像压缩的新方法。为了实现高图像压缩率并保留图像信息,将运动检测和小波变换结合在一起。第一步,确定感兴趣区域(ROI),然后将图像分为几个(8x8像素)块。此后,估计每个块的运动矢量以预测随后的帧(预测的模型帧)。另外,通过将小波变换应用于主图像(应进行预测)来创建小波分量;而提取的小波分量被用作预测帧误差补偿器。随后,提取每个块的运动矢量的熵作为确定用于小波帧量化的量化级别的标准。基于劳埃德算法对小波帧进行量化。最后,计算预测模型帧的每个块与主图像的相应块之间的相关性,以评估结果的准确性。如果计算的相关性大于0.5,则将预测模型和小波帧的相应块的优化组合用作最终块。否则,将小波帧的块视为最终结果。使用PSNR,MSE,GLCM和基于专家的质量图像验证对结果进行了分析。将该算法与标准的MPEG标准H.264和VC1进行了比较,证明了该算法的性能。

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