A Multiparametric Class of Low-complexity Transforms for Image and Video Coding
This work addresses the need for efficient data compression in low-complexity and low-power systems, such as in image and video coding standards, but is incremental as it builds on existing DCT approximation methods.
The authors introduced a new class of low-complexity 8-point DCT approximations for image and video coding, solving a multicriteria optimization to select best-performing transforms that show compelling results in coding efficiency and image quality metrics while requiring only few addition or bit-shifting operations.
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.