A Multiparametric Class of Low-complexity Transforms for Image and Video Coding
arXiv:2006.11418 · doi:10.1016/j.sigpro.2020.107685
Abstract
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.
Fixed Figure 1 and typos in the reference list
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Cited by in corpus (5)
- A Class of Low-complexity DCT-like Transforms for Image and Video Coding
- Low-complexity Rounded KLT Approximation for Image Compression
- Extensions on Low-complexity DCT Approximations for Larger Blocklengths Based on Minimal Angle Similarity
- Data-independent Low-complexity KLT Approximations for Image and Video Coding
- Fast Data-independent KLT Approximations Based on Integer Functions