The peak signal-to-noise ratio (PSNR) is one of the most popular video quality metrics. This ratio is computed using both original and processed images. We propose a new method to estimate the PSNR from an encoded bit stream without using original video sequences. In the proposed method, the transform coefficients of images or video frames are modeled by a generalized Gaussian distribution. By utilizing the model parameters of this distribution, the PSNR can be estimated. We also propose a fast method that can be used to estimate the model parameters of the original transform coefficient distribution using quantized transform coefficients as well as quantization information extracted from encoded bit streams. Experimental results with H.264 bit streams show that the proposed generalized Gaussian modeling method delivers better performance compared to the standard Laplacian modeling method when estimating the PSNR. The proposed method can be applied to image or video streams compressed with standard coding algorithms, such as MPEG-1, 2, 4, H.264, and JPEG. The proposed method can also be used for image or video quality monitoring systems on the receiver's side.

Estimation of the peak signal-to-noise ratio for compressed video based on generalized Gaussian modeling

Between the two main schemes of digital video coding, VBR encoding scheme is generally considered as better in terms of efficiency and encoding quality in comparison to CBR, because it retains the same quantization parameters for the whole encoding procedure (unconstrained VBR), without altering them according to a specific adaptive rate algorithm. Towards this generally accepted statement, this paper presents a quantitative comparison to the perceptual efficiency of VBR over CBR for Moving Picture Expert Group – 4 (MPEG-4) ASP CIF and QCIF encoding sequences, showing that the VBR does not out-perform significantly the corresponding CBR encoding quality, since the deduced perceptual advantage/ratio of VBR over CBR for CIF is approximately 4-5% and constant for all the encoding bit rates greater than 200 kbps, while for the QCIF-case the relative ratio drops to approximately 2.5%.

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Quantitative Perceptual Comparison of Variable Bit Rate over Constant Bit Rate Encoding Scheme for MPEG-4 Video

Laser medicine is one of the earliest developed and most prominent branch of applications of laser technology. Since the first appearance of the ruby laser in 1960 it took only one year for the ruby laser retina coagulation machine to be found in the market. Accompanying the development of various species of new lasers, together with the development of fiber techniques and applications of electronics, tremendous progress has taken place on laser applications to biological and medical basic research as well as to laser medical treatment and diagnostics. Indeed, laser medicine is a multi-disciplined new technology and profession, and much more important developments are expected.

DaHeng Wang

Papers

Laser medicine in China