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Recently, many researches on convolution neural network (CNN) based in-loop filters have been proposed to improve coding efficiency. However, most existing CNN based filters tend to train and deploy multiple networks for various quantization parameters (QP) and frame types (FT), which drastically increases resources in training these models and the memory burdens for video codec. In this paper, we propose a novel variable CNN (VCNN) based in-loop filter for VVC, which can effectively handle the compressed videos with different QPs and FTs via a single model. Specifically, an efficient and flexible attention module is developed to recalibrate features according to QPs or FTs. Then we embed the module into the residual block so that these informative features can be continuously utilized in the residual learning process. To minimize the information loss in the learning process of the entire network, we utilize a residual feature aggregation module (RFA) for more efficient feature extraction. Based on it, an efficient network architecture VCNN is designed that can not only effectively reduce compression artifacts, but also can be adaptive to various QPs and FTs. To address training data imbalance on various QPs and FTs and improve the robustness of the model, a focal mean square error loss function is employed to train the proposed network. Then we integrate the VCNN into VVC as an additional tool of in-loop filters after the deblocking filter. Extensive experimental results show that our VCNN approach obtains on average 3.63%, 4.36%, 4.23%, 3.56% under all intra, low-delay P, low-delay, and random access configurations, respectively, which is even better than QP-Separate models. [ABSTRACT FROM AUTHOR]

With a lot of enhanced coding tools introduced, High Efficiency Video Coding (HEVC) achieves significant improvement in coding efficiency at the cost of increased computational complexity. To efficiently reduce the encoding time of HEVC, a wavefront-based high parallel (WHP) solution integrating novel data-level and task-level methods is proposed in this paper. On data level, optimal single-instruction-multiple-data algorithms are designed for the enhanced coding tools, i.e., replacing the multiplication in motion compensation by add and shift operations with reduced instruction cycles, removing the transpose in transform via realignment of coefficients, and minimizing the memory access in sum of absolute difference/sum of squared differences calculation by fully reusing the registers. On task level, a novel inter-frame wavefront (IFW) method is developed by effectively decreasing the dependence of wavefront parallel processing (WPP). In addition, a coding tree block level parallelism analysis method is presented to prove the superior of IFW method compared with other HEVC representative parallel methods. Besides, a three-level thread management scheme is proposed to best exploit the parallelism of IFW method and achieve corresponding encoding speedup. Extensive experimental results show that, the overall WHP solution can bring up to $57.65\times $ , $65.55\times $ , and $88.17\times $ speedup for HEVC encoding of Wide Video Graphics Array, 720p and 1080p standard test sequences, while maintaining the same coding performance as with WPP. The proposed solution is also applied in several leading video companies in China, providing HEVC video service for more than 1.3 million users everyday. [ABSTRACT FROM PUBLISHER]

Foreground detection is an important task in computer vision applications. In this paper, we present an efficient foreground detection method based on a robust linear regression model. First, a novel framework is proposed where foreground detection has been cast as an outlier signal estimation problem in a linear regression model. We regularize this problem by imposing a so-called fused sparsity constraint, which encourages both sparsity and smoothness of vector coefficients, on the outlier signal. Second, we convert this outlier signal estimation problem into an equivalent Fused Lasso problem, and then use existing solutions to obtain an optimized solution. Third, a new foreground detection method is presented to apply this new model to the 2-D image domain by merging the results from different vectorizations. Experiments on a set of challenging sequences show that the proposed method is not only superior to many state-of-the-art techniques, but also robust to noise. [ABSTRACT FROM PUBLISHER]