论文标题
用于图像和视频编码的低复杂性变换的多参数类
A Multiparametric Class of Low-complexity Transforms for Image and Video Coding
论文作者
论文摘要
离散转换在许多信号处理应用中起着重要的作用，而近年来，低复杂性的经典变换替代方案变得很流行。尤其是，已证明离散的余弦变换（DCT）在数据压缩方面很方便，用于众所周知的图像和视频编码标准，例如JPEG，H.264以及最近的高效率视频编码（HEVC）。在本文中，我们基于Bouguezel，Ahmed和Swamy发表的一系列作品，介绍了一类新的低复杂性8点DCT近似值。同样，得出了包含已知和新型变换的多参数快速算法。我们在解决多标准优化问题后选择表现最佳的DCT近似值，并将其提交为获得更大尺寸变换的缩放方法。我们在类似JPEG的图像压缩和视频编码实验中评估了这些DCT近似值。我们表明，最佳DCT近似在编码效率和图像质量指标方面具有令人信服的结果，并且仅需要少量添加或位移动操作，适用于低复杂性和低功率系统。
Discrete transforms play an important role in many signal processing applications, and low-complexity alternatives for classical transforms became popular in recent years. Particularly, the discrete cosine transform (DCT) has proven to be convenient for data compression, being employed in well-known image and video coding standards such as JPEG, H.264, and the recent high efficiency video coding (HEVC). In this paper, we introduce a new class of low-complexity 8-point DCT approximations based on a series of works published by Bouguezel, Ahmed and Swamy. Also, a multiparametric fast algorithm that encompasses both known and novel transforms is derived. We select the best-performing DCT approximations after solving a multicriteria optimization problem, and submit them to a scaling method for obtaining larger size transforms. We assess these DCT approximations in both JPEG-like image compression and video coding experiments. We show that the optimal DCT approximations present compelling results in terms of coding efficiency and image quality metrics, and require only few addition or bit-shifting operations, being suitable for low-complexity and low-power systems.
