In order to enhance the accuracy of the Motion Vector (MV) Estimation and also reduce the error propagation issue during the Estimation, in this paper, a new adaptive error concealment (EC) approach is proposed based on the information extracted from the video scene. In this regard, the Motion information of the video scene around the degraded MB is first analyzed to estimate the Motion type of the degraded MB. If the neighboring MBs possess uniform Motion, the degraded MB imitates the behavior of neighboring MBs by choosing the MV of the collocated MB. Otherwise, the lost MV is estimated through the second proposed EC technique (i. e., IOBMA). In the IOBMA, unlike the conventional boundary matching criterion-based EC techniques, not only each boundary distortion is evaluated regarding both the luminance and the chrominance components of the boundary pixels, but also the total boundary distortion corresponding to each candidate MV is calculated as the weighted average of the available boundary distortions. Compared with the state-of-the-art EC techniques, the simulation results indicate the superiority of the proposed EC approach in terms of both the objective and subjective quality assessments.

In this paper a new method for determining the search area for Motion Estimation algorithm based on block matching is suggested. In the proposed method the search area is adaptively found for each block of a frame. This search area is similar to that of the full search (FS) algorithm but smaller for most blocks of a frame. Therefore, the proposed algorithm is analogous to FS in terms of regularity but has much less computational complexity. To find the search area, the temporal and spatial correlations among the Motion Vectors of blocks are used. Based on this, the matched block is chosen from a rectangular area that the prediction Vectors set out. Simulation results indicate that the speed of the proposed algorithm is at least 7 times better than the FS algorithm.      

An important technique in image and video processing is global Motion Estimation (GME). The common GME methods can be classified in direct and indirect categories. Whereas the direct global Motion Estimation techniques boast reasonable precision they tend to suffer from high complexity. As with indirect methods, though presenting lower complexity, they mostly exhibit lower accuracy than their direct counterparts. In this paper, the authors introduce a robust algorithm for GME with near identical accuracy and almost 50-times faster than MPEG-4 verification model (VM). This approach entails two stages in which, first, Motion Vector of sampled block is employed to obtain initial GME then Levenberg-Marquardt algorithm is applied to the subsampled pixels to optimize the initial GME values. As will be shown, the proposed solution exhibits remarkable accuracy and speed features with experimental results distinctively bearing them out.

Transmission of compressed video over error prone channels may result in packet losses, which can degrade the image quality. Error concealment (EC) is an effective approach to reduce the degradation caused by the missed information. The conventional temporal EC techniques are always inefficient when the Motions of the video object are irregular. In this paper, in order to overcome this problem, an efficient temporal EC approach to conceal the macroblock error for video coding systems is proposed. The proposed EC method employs a RBF neural network to estimate the Motion Vectors of the damaged macroblocks. RBF estimator is used only for areas of the fast Motions, which reduces computation complexity. Because the neural networks have a great capacity for visualizing and interpreting high-dimensional data sets, the Estimation model proposed herein can exploit the nonlinearity property of the neural networks to estimate lost Motion Vectors more accurately. Simulation results show that the proposed technique enhances both subjective and objective quality of reconstructed frames, such as the average PSNR increases about 1.5 dB compared to the BMA method for the test video sequences in some frames.