This paper introduces row-column transforms (RCTs) which are 2-D) non-separable transforms defined with the aid of a set of 1-D linear transforms and a basis ordering permutation. We propose a novel method for the des...
详细信息
ISBN:
(纸本)9781467399616
This paper introduces row-column transforms (RCTs) which are 2-D) non-separable transforms defined with the aid of a set of 1-D linear transforms and a basis ordering permutation. We propose a novel method for the design of row-column transforms that approximate desired complex transforms (such as KLTs, SOTs, etc.) so that most of the performance of the approximated transforms is retained at significantly reduced complexity. Given a non-separable block transform of interest, our method designs an RCT by (i) optimizing a set of 1-D transforms applied to rows and columns of the signal block and OH finding the best transform coefficient ordering permutation. Our experimental results show that optimized RCTs closely approach the compression performance of the desired non-separable transforms while retaining the computational complexity of separable transforms.
This paper introduces row-column transforms (RCTs) which are 2-D non-separable transforms defined with the aid of a set of 1-D linear transforms and a basis ordering permutation. We propose a novel method for the desi...
详细信息
ISBN:
(纸本)9781467399623
This paper introduces row-column transforms (RCTs) which are 2-D non-separable transforms defined with the aid of a set of 1-D linear transforms and a basis ordering permutation. We propose a novel method for the design of row-column transforms that approximate desired complex transforms (such as KLTs, SOTs, etc.) so that most of the performance of the approximated transforms is retained at significantly reduced complexity. Given a non-separable block transform of interest, our method designs an RCT by (i) optimizing a set of 1-D transforms applied to rows and columns of the signal block and (ii) finding the best transform coefficient ordering permutation. Our experimental results show that optimized RCTs closely approach the compression performance of the desired non-separable transforms while retaining the computational complexity of separable transforms.
We introduce layered-Givens transforms (LGTs) which are arbitrary dimensional, tunable-complexity, orthonormal transforms of data. LGTs are formed by layers of data permutations and Givens rotations with the number of...
详细信息
ISBN:
(纸本)9781509021758
We introduce layered-Givens transforms (LGTs) which are arbitrary dimensional, tunable-complexity, orthonormal transforms of data. LGTs are formed by layers of data permutations and Givens rotations with the number of layers controlling the overall transform's computational complexity. We propose a novel method for the design of layered-Givens transforms that approximate desired complex transforms (such as MA's, SOTs, etc.) so that most of the performance of the approximated transform is retained at significantly reduced complexity. Key to the LGT performance is the choice of the permutations that arrange the data prior to the Givens rotations. Despite the very highly combinatorial nature of the LGT design problem, we provide an algorithm that finds the optimal parameters (permutation and Givens rotations) for each layer. Our results show that designed LGTs closely approximate desired non-separable transforms at significantly reduced complexity.
暂无评论