Efficient Deblocking With Coefficient Regularization, Shape-Adaptive Filtering, and Quantization Constraint

Efficient Deblocking With Coefficient Regularization, Shape-Adaptive Filtering, and Quantization Constraint

We propose an effective deblocking scheme with extremely low computational complexity. The algorithm involves three parts: local ac coefficient regularization (ACR) of shifted blocks in the discrete cosine transform (DCT) domain, block-wise shape adaptive filtering (BSAF) in the spatial domain, and quantization constraint (QC) in the DCT domain. The DCT domain ACR suppresses the grid noise (blockiness) in monotone areas. The spatial-domain BSAF alleviates the staircase noise along the edge, and the ringing near the edge and the corner outliers. The narrow quantization constraint set is imposed to prevent possible oversmoothing and improve PSNR performance. Extensive simulation results and comparative studies are provided to justify the effectiveness and efficiency of the proposed deblocking algorithm.

我们提出一种计算复杂度极低的有效去块效应方案。该算法包括三个部分：离散余弦变换（DCT）域中移位块的局部交流系数正则化（ACR）、空间域中的块形状自适应滤波（BSAF）以及DCT域中的量化约束（QC）。DCT域的ACR抑制单调区域中的网格噪声（块效应）。空间域的BSAF减轻沿边缘的阶梯噪声以及边缘附近的振铃和角落异常值。施加窄量化约束集是为了防止可能的过度平滑并提高峰值信噪比（PSNR）性能。提供了大量的模拟结果和对比研究，以证明所提出的去块算法的有效性和高效性。