A Class of DCT Approximations Based on the Feig-Winograd Algorithm
Not on the map. Why?
Abstract (arXiv)
A new class of matrices based on a parametrization of the Feig-Winograd factorization of 8-point DCT is proposed. Such parametrization induces a matrix subspace, which unifies a number of existing methods for DCT approximation. By solving a comprehensive multicriteria optimization problem, we identified several new DCT approximations. Obtained solutions were sought to possess the following properties: (i) low multiplierless computational complexity, (ii) orthogonality or near orthogonality, (iii) low complexity invertibility, and (iv) close proximity and performance to the exact DCT. Proposed approximations were submitted to assessment in terms of proximity to the DCT, coding performance, and suitability for image compression. Considering Pareto efficiency, particular new proposed approximations could outperform various existing methods archived in literature.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Feb 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 3; 2023: 0; 2024: 1.
4 papers.
- 2022DCT Approximations Based on Chen's Factorizationcited by 1
- 2022Low-complexity Approximate Convolutional Neural Networkscited by 1
- 2022Low-Complexity Loeffler DCT Approximations for Image and Video Codingcited by 0
- 2024Extensions on Low-complexity DCT Approximations for Larger Blocklengths Based on Minimal Angle Similaritycited by 0
- 2024Extensions on Low-complexity DCT Approximations for Larger Blocklengths Based on Minimal Angle Similarity20 Oct 2024
- 2022Low-complexity Approximate Convolutional Neural Networks29 Jul 2022
- 2022Low-Complexity Loeffler DCT Approximations for Image and Video Coding29 Jul 2022
- 2022DCT Approximations Based on Chen's Factorization24 Jul 2022