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The elastic motion estimation is an efficient temporal predictive coding tool for predicting complex motions including zooming, shearing and twisting. However, its optimization solution still suffers from high computational complexity and unstable convergence yet. We thus present a novel optimization framework to improve the elastic motion estimation efficiency. By introducing a diagonal matrix into the Gauss-Newton’s Hessian matrix, we employ the Levenberg-Marquardt optimization to compute the elastic motion vectors. It obtains superior convergence to the conventional Gauss-Newton’s method, especially when the starting point is far away from the globally/locally optimum. Furthermore, we prove that the Gauss-Newton’s Hessian matrix for elastic motion estimation is positive semi-definite by discussing the convexity of the motion-compensated error function. Such a finding allows us to adopt a sign-alternating factor, instead of a positive constant used in the traditional L-M method, to update iteratively the diagonal matrix coefficient. And a bound is determined for the diagonal matrix coefficient to guarantee the non-singularity of the Hessian matrix, with whose constraints we propose an adaptive method to compute the sign-alternating factor to restrict the iterative process into a reasonable motion vector space. Finally, a novel elastic motion estimation algorithm is presented based on the modified Levenberg-Marquardt optimization. Experimental results show that the proposed algorithm gains higher motion-compensated peak signal-to-noise ratio (PSNR) than several benchmark methods. Moreover, our algorithm converges fast which needs only 1-2 iterations before it achieves higher PSNR than conventional elastic motion estimation.

Motion estimation is widely used for video compression as it reduces the memory requirements of any video file while maintaining high visual quality. But the computational cost consumed by motion estimation/compensation usually accounts for more than 50%, even to 80%, of the total cost of the encoder. So researchers try to develop fast motion estimation algorithms to lower the computational complexity. Moreover, few studies have been concentrated on the efficient motion estimation of arbitrarily shaped visual objects, which particularly pays insufficient attention to boundary continuity of the motion-compensated objects as well. In this research, we present a hierarchical wavelet-domain approach to motion estimation of visual objects with arbitrary shapes. We explore the guiding role of binary alpha-plane in assisting motion estimation of visual objects, and bring forward a binary alpha-plane matching scheme. It constrains the intensity-based motion estimation into a small subset of initial candidate vectors in the search window. This both accelerates search speed and helps to produce coherent object boundary. Moreover, we employ the low-band-shift technique to eliminate the wavelet’s shift-variance effect. And based on the observation that high frequency subbands of wavelet contribute little to calculating motion vectors, a new matching function is derived for fast wavelet-domain motion estimation. Coupled with the alpha-plane matching and the devised matching function, a hierarchical low-band-based motion estimation approach is further proposed. Through executing a refined search around the initial candidate vectors using only the low frequency subband at each scale, our approach achieves a satisfying balance between prediction accuracy and computational efficiency. Experimental results show that by using the standard accuracy measurements and the benchmark video sequences, the performance of the proposed approach is close to the full search with a matching quality over 99%, and with 95.80% reduction in complexity.

Topic model is a powerful tool for the basic document or image processing tasks. In this study we introduce a novel image topic model, called Latent Patch Model (LPM), which is a generative Bayesian model and assumes that the image and pixels are connected by a latent patch layer. Based on the LPM, we further propose an image denoising algorithm namely multiple estimate LPM (MELPM). Unlike other works, the proposed denoising framework is totally implemented on the latent patch layer, and it is effective for both Gaussian white noises and impulse noises. Experimental results demonstrate that LPM performs well in representing images. And its application in image denoising achieves competitive PSNR and visual quality with conventional algorithms.

This study proposes a novel fuzzy quantization based bit transform for low bit-resolution motion estimation. We formalize the procedure of bit resolution reduction by two successive steps, namely interval partitioning and interval mapping. The former is a many-to-one mapping which determines motion estimation performance, while the latter is a one-to-one mapping. To gain a reasonable interval partitioning, we propose a non-uniform quantization method to compute coarse thresholds. They are then refined by using a membership function to solve the mismatch of pixel values near threshold caused by camera noise, coding distortion, etc. Afterwards, we discuss that the sum of absolute difference (SAD) is one of the fast matching metrics suitable for low bit-resolution motion estimation in the sense of mean squared errors. A fuzzy quantization based low bit-resolution motion estimation algorithm is consequently proposed. Our algorithm not only can be directly employed in video codecs, but also be applied to other fast or complexity scalable motion estimation algorithms. Extensive experimental results show that the proposed algorithm can always achieve good motion estimation performances for video sequences with various characteristics. Compared with one-bit transform, multi-thresholding two-bit transform, and adaptive quantization based two-bit transform, our bit transform separately gains 0.98dB, 0.42dB, and 0.24dB improvement in terms of average peak signal-to-noise ratio, with less computational cost as well.