基于Feig-Winograd算法的一类DCT近似方法
A Class of DCT Approximations Based on the Feig-Winograd Algorithm
AI总结：
本文提出基于Feig-Winograd算法8点DCT因子化的参数化矩阵类，通过多目标优化获得具有低计算复杂度、正交性、低逆复杂度及接近精确DCT性能的新型DCT近似方法。
摘要：
本文提出了一种基于Feig-Winograd因子化8点DCT的参数化矩阵类。此类参数化诱导出一个矩阵子空间，统一了多种现有的DCT近似方法。通过求解一个综合的多目标优化问题，我们识别出几种新的DCT近似方法。获得的解旨在具备以下特性：(i) 低乘法无计算复杂度，(ii) 正交或近正交性，(iii) 低复杂度可逆性，(iv) 接近精确DCT的接近性和性能。所提出的方法在接近DCT、编码性能及图像压缩适用性方面进行了评估。考虑到帕累托效率，某些新提出的近似方法可能在文献中已有的各种现有方法上表现更优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
