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首页> 外文会议>Conference on Stereoscopic Displays and Applications; 20080128-30; San Jose,CA(US) >Stereoscopic Image Quality Metrics and Compression
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Stereoscopic Image Quality Metrics and Compression

机译：立体图像质量指标和压缩

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摘要

We are interested in metrics for automatically predicting the compression settings for stereoscopic images so that we can minimize file size, but still maintain an acceptable level of image quality. Initially we investigate how Peak Signal to Noise Ratio (PSNR) measures the quality of varyingly coded stereoscopic image pairs. Our results suggest that symmetric, as opposed to asymmetric stereo image compression, will produce significantly better results. However, PSNR measures of image quality are widely criticized for correlating poorly with perceived visual quality. We therefore consider computational models of the Human Visual System (HVS) and describe the design and implementation of a new stereoscopic image quality metric. This point matches regions of high spatial frequency between the left and right views of the stereo pair and accounts for HVS sensitivity to contrast and luminance changes in regions of high spatial frequency, based on Michelson's Formula and Peli's Band Limited Contrast Algorithm. To establish a baseline for comparing our new metric with PSNR we ran a trial measuring stereoscopic image encoding quality with human subjects, using the Double Stimulus Continuous Quality Scale (DSCQS) from the ITU-R BT.500-11 recommendation. The results suggest that our new metric is a better predictor of human image quality preference than PSNR and could be used to predict a threshold compression level for stereoscopic image pairs.
机译：我们对自动预测立体图像的压缩设置的指标感兴趣，这样我们可以最小化文件大小，但仍保持可接受的图像质量水平。最初，我们研究峰值信噪比（PSNR）如何测量变化编码的立体图像对的质量。我们的结果表明，与非对称立体声图像压缩相反，对称将产生明显更好的结果。但是，人们普遍批评图像质量的PSNR度量与感知的视觉质量之间的关联不佳。因此，我们考虑了人类视觉系统（HVS）的计算模型，并描述了一种新的立体图像质量度量的设计和实现。此点匹配立体声对的左右视图之间的高空间频率区域，并基于迈克尔逊公式和Peli频带有限对比度算法，考虑了HVS对高空间频率区域的对比度和亮度变化的敏感性。为了建立将我们的新指标与PSNR进行比较的基准，我们进行了一项试验，使用ITU-R BT.500-11建议书中的双刺激连续质量量表（DSCQS）来测量人类受试者的立体图像编码质量。结果表明，我们的新指标比PSNR更好地预测了人类图像质量，并且可以用于预测立体图像对的阈值压缩级别。

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