Single step optimal block matched motion estimation with motion vectors having arbitrary pixel precisions

This paper proposes a non-linear block matched motion model and solves the motion vectors with arbitrary pixel precisions in a single step. As the optimal motion vector which minimizes the mean square error is solved analytically in a single step, the computational complexity of our proposed algorithm is lower than that of conventional quarter pixel search algorithms. Also, our proposed algorithm can be regarded as a generalization of conventional half pixel search algorithms and quarter pixel search algorithms because our proposed algorithm could achieve motion vectors with arbitrary pixel precisions

Single step optimal block matched motion estimation with motion vectors having arbitrary pixel precisions

This paper proposes a non-linear block matched motion model with motion vectors having arbitrary pixel precisions. The optimal motion vector which minimizes the mean square error is solved analytically in a single step. Our proposed algorithm can be regarded as a generalization of conventional half pixel search algorithms and quarter pixel search algorithms because our proposed algorithm could achieve motion vectors with arbitrary pixel precisions. Also, the computational effort of our proposed algorithm is lower than that of conventional quarter pixel search algorithms because our proposed algorithm could achieve motion vectors in a single step

Optimal cosine modulated nonuniform linear phase FIR filter bank design via stretching and shifting frequency response of prototype filter

This paper proposes an optimal cosine modulated nonuniform linear phase finite impulse response (FIR) filter bank design. The frequency responses of all the analysis filters and the synthesis filters of the filter bank are derived based on both stretching and shifting the frequency response of the prototype filter. The total aliasing error of the filter bank is minimized subject to a specification on the maximum amplitude distortion of the filter bank as well as specifications on both the maximum passband ripple magnitude and the maximum stopband ripple magnitude of the prototype filter. This filter bank design problem is actually a functional inequality constrained optimization problem. Our recently developed integration approach is employed for solving the problem. Computer numerical simulation results show that our proposed design method outperforms existing design methods

Two-channel linear phase FIR QMF bank minimax design via global nonconvex optimization programming

In this correspondence, a two-channel linear phase finite impulse response (FIR) quadrature mirror filter (QMF) bank minimax design problem is formulated as a nonconvex optimization problem so that a weighted sum of the maximum amplitude distortion of the filter bank, the maximum passband ripple magnitude and the maximum stopband ripple magnitude of the prototype filter is minimized subject to specifications on these performances. A modified filled function method is proposed for finding the global minimum of the nonconvex optimization problem. Computer numerical simulations show that our proposed design method is efficient and effective

On relationship of Z-curve and Fourier approaches for DNA coding sequence classification

Z-curve features are one of the popular features used in exon/intron classification. We showed that although both Z-curve and Fourier approaches are based on detecting 3-periodicity in coding regions, there are significant differences in their spectral formulation. From the spectral formulation of the Z-curve, we obtained three modified sequences that characterize different biological properties. Spectral analysis on the modified sequences showed a much more prominent 3-periodicity peak in coding regions than the Fourier approach. For long sequences, prominent peaks at 2Π/3 are observed at coding regions, whereas for short sequences, clearly discernible peaks are still visible. Better classification can be obtained using spectral features derived from the modified sequences

Cross chromosomal similarity for DNA sequence compression

Current DNA compression algorithms work by finding similar repeated regions within the DNA sequence and then encoding these regions together to achieve compression. Our study on chromosome sequence similarity reveals that the length of similar repeated regions within one chromosome is about 4.5% of the total sequence length. The compression gain is often not high because of these short lengths. It is well known that similarity exist among different regions of chromosome sequences. This implies that similar repeated sequences are found among different regions of chromosome sequences. Here, we study cross-chromosomal similarity for DNA sequence compression. The length and location of similar repeated regions among the sixteen chromosomes of S. cerevisiae are studied. It is found that the average percentage of similar subsequences found between two chromosome sequences is about 10% in which 8% comes from cross-chromosomal prediction and 2% from self-chromosomal prediction. The percentage of similar subsquences is about 18% in which only 1.2% comes from self-chromosomal prediction while the rest is from cross-chromosomal prediction among the 16 chromosomes studied. This suggests the importance of cross-chromosomal similarities in addition to self-chromosomal similarities in DNA sequence compression. An additional 23% of storage space could be reduced on average using self-chromosomal and cross-chromosomal predictions in compressing the 16 chromosomes of S. cerevisiae

Generalized approach for the realization of discrete cosine transform using cyclic convolutions

A general solution is proposed to realize the discrete cosine transform of any length via cyclic convolutions in this paper. This algorithm is not opt imal in minimizing any measure of computational complexity, but it involves some regular forms that are most suitable for the realization using technologies and structures which are well suited for doing convolutions, such as the distributed arithmetic and the systolic array. On the other hand, this algorithm is much more flexible than any available D C r algorithm as it can be applied to realize DCT/IDCT with any length

Intelligent Painter: Picture Composition With Resampling Diffusion Model

Have you ever thought that you can be an intelligent painter? This means that you can paint a picture with a few expected objects in mind, or with a desirable scene. This is different from normal inpainting approaches for which the location of specific objects cannot be determined. In this paper, we present an intelligent painter that generate a person's imaginary scene in one go, given explicit hints. We propose a resampling strategy for Denoising Diffusion Probabilistic Model (DDPM) to intelligently compose unconditional harmonized pictures according to the input subjects at specific locations. By exploiting the diffusion property, we resample efficiently to produce realistic pictures. Experimental results show that our resampling method favors the semantic meaning of the generated output efficiently and generates less blurry output. Quantitative analysis of image quality assessment shows that our method produces higher perceptual quality images compared with the state-of-the-art methods.Comment: ICIP 202