9933284
A MCMC approach for Bayesian super-resolution image reconstruction
In this paper, we consider the super-resolution image reconstruction problem. We propose a Markov chain Monte Carlo (MCMC) approach to find the maximum a posterior probability (MAP) estimation of the unknown high-resolution image. Firstly, Gaussian Markov random field (GMRF) is exploited for modeling the prior probability distribution of the unknown high-resolution image. Then, a MCMC technique (in particular, the Gibbs sampler) is introduced to generate samples from the posterior probability distribution to compute the MAP estimation of the unknown high-resolution image, which is obtained as the mean of the samples. Moreover, we derive a bound on the convergence time of the proposed MCMC approach. Finally, the experimental results are presented to verify the superior performance of the proposed approach and the validity of the proposed bound.
1533691
Generalized optimum dynamic bit allocation scheme for source compression
Dynamic bit allocation directly impacts the overall coding performance in block transform (e.g., DCT, Hadamard, etc.) coding, subband coding, and wavelet coding. In very low bit rate coding (such as the evolving MPEG-4 standard), additional factors impacting the bit allocation process need to be identified and properly exploited to achieve optimal coding performance. In the paper, a generalized optimum dynamic bit allocation algorithm is presented. The algorithm is based on the Shannon rate-distortion bound and a weighted distortion criterion using a Lagrange multiplier optimization technique. The results look promising for applications in real-time multimedia communications.<<ETX>>
35556766
Error concealment for image transmission by multiscale Markov random field modeling
Transmission of visual data over communication channels introduces serious fidelity problems. The paper proposes an efficient error concealment algorithm for the image transmitted as encoded bitstreams. The proposed method introduces a multiscale Markov random field (MMRF) to model the original image. The established MMRF model is consequently employed to interpolate the missing blocks in the observing data via maximum a posterior (MAP) estimation. Compared with the conventional spatial interpolation scheme, this algorithm offers significant improvements on both subjective and objective qualities of the recovered image.
6762785
A new diamond search algorithm for fast block-matching motion estimation
Based on the study of motion vector distribution from several commonly used test image sequences, a new diamond search (DS) algorithm for fast block-matching motion estimation (BMME) is proposed in this paper. Simulation results demonstrate that the proposed DS algorithm greatly outperforms the well-known three-step search (TSS) algorithm. Compared with the new three-step search (NTSS) algorithm, the DS algorithm achieves close performance but requires less computation by up to 22% on average. Experimental results also show that the DS algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.
2569570
Practical issues of dynamic bit allocation in multimedia source compression
Owing to various emerging multimedia applications which create enormous amount of data traffic over limited channel bandwidth, dynamic bit allocation plays a significant role on achieving robust and/or optimum source coding performance. In this paper, we focus on pixel-based and perceptual-based dynamic bit allocations which have been widely exploited in many source coding algorithms and international audio/visual compression standards as well. Many fundamental facts of human auditory system have been effectively exploited in perceptual-based bit allocation for speech and audio signals. On the contrary, pixel-based bit allocation which is widely used in image and video coding has certain practical issues required to be addressed. We highlight these observed concerns, present an unified and optimum solution, and recommend a future direction.
2488876
Tri-state median filter for image denoising
In this work, a novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise. We incorporate the standard median (SM) filter and the center weighted median (CWM) filter into a noise detection framework to determine whether a pixel is corrupted, before applying filtering unconditionally. Extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation.
5064603
The ascending star subgraph decomposition of some star forests