An effective compression artifacts removal algorithm is proposed based on the theory of projections onto convex sets (POCS). It includes a block classification procedure, a ringing detection procedure, prediction of the spatial distribution of the quantization errors and estimation of the visibility of the compression artifacts. Information gained from both the spatial and transform domains, is incorporated into adaptive projections. Experiments performed on JPEG-compressed images, demonstrate the effectiveness of the proposed algorithm in suppressing both blocking and ringing artifacts, as well as the ability of the algorithm to preserve the image sharpness.
The linear regression and correlation analysis of daily returns of several stocks and stock-exchange index at Macedonian Stock Exchange (MSE) provide evidence for statistical significance of the stocks' daily returns at MSE.Statistical analysis was focused to determine the character of relationship between the 10 most liquid stocks at MSE using ten-year time-series of daily stocks' closing price and for the Macedonian Stock Exchange Index (MBI-10).The analysis of daily stock returns provided R 2 values and confirmed that the proportion of the total correlation in the dependent variable (one stock price) can be explained by the independent variable (other stock price) as well as that accurate forecasting of one stock price movements enables reliable prediction of other stock future price at MSE. Some implications for stock valuation are drawn.
Modern video surveillance and target tracking applications utilize multiple cameras transmitting low-bit-rate video through channels of very limited bandwidth. The highly compressed video exhibits coding artifacts that can cause target detection and tracking procedures to fail. Thus, to lower the level of noise and retain the sharpness of the video frames, super-resolution techniques can be employed for video enhancement. In this paper, we propose an efficient super-resolution video enhancement scheme that is based on a constrained set of motion vectors. The proposed scheme computes the motion vectors using the original (uncompressed) video frames, and transmits only a small set of these vectors to the receiver. At the receiver, each pixel is assigned a motion vector from the constrained set to maximize the motion prediction performance. The size of the transmitted vector set is constrained to be less than 3% of the total coded bit stream. In the video enhancement process, an L2-norm minimization super-resolution procedure is applied. The proposed scheme is applied to enhance highly compressed, real-world video sequences. The results obtained show significant improvement in the visual quality of the video sequences, as well as in the performance of subsequent target detection and tracking procedures.
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