Hee Suk Pang

Spiral intra macroblock refresh with motion vector restriction for low bit-rate video telephony over a 3G network

A method to limit the effect of error propagation in low bit-rate error resilient video coding has been proposed and implemented on a 3G prototype videophone. In conventional methods, the effect of the bit-stream error does not vanish rapidly since the refreshed macroblocks are affected by the errors in the unrefreshed macroblocks. To overcome this ineffectiveness, two techniques are used; one is motion vector restriction that limits the error propagation, and the other is the spiral intra macroblock refresh that minimizes coding efficiency degradation caused by the motion vector restriction. Further, the latter technique concentrates on the center area in the screen, where the subjective preference is closely related. Experiments show that the proposed method removes the effect of the error more rapidly than the conventional methods.

Time Delay Estimation Method Based on Canonical Correlation Analysis

The localization of sources has numerous applications. To find the position of sources, the relative delay between two or more received signals for the direct signal must be determined. The generalized cross-correlation method is the most popular technique; however, an approach based on eigenvalue decomposition (EVD) is another popular one that utilizes the eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in low SNR and reverberation, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) to extend the operation SNR to the lower SNR and reverberation. The proposed algorithm uses an eigenvector that corresponds to the maximum eigenvalue in the generalized eigenvalue equation (GEVD). The estimated eigenvector contains all required information for time delay estimation. We have performed simulations with uncorrelated, correlated noise and reverberation for several SNRs, to show that time delays can be more accurately estimated (especially for low SNR) a CCA based algorithm versus the adaptive EVD algorithm.

Single reference super-resolution using inter-subband correlation of directional edge

We propose an efficient single reference superresolution system based on the discrete wavelet transform. We assume that the low-resolution image to be enhanced is the low-frequency subband of the high-resolution image to be reconstructed. We design a support vector machine synthesizing the high-frequency subband based on the inter-subband correlation of the directional edges. Experimental results of sample images show that the proposed system offers improvements in terms of both measured distortion and subjective appearance.

On the Window Selection for Three FFT-Based High-Accuracy Frequency Estimation Methods

Recent studies show that several FFT-based high-accuracy frequency estimation methods achieve very good performance. In this letter, we select three methods, which are the zero-padding, weighted multipoint interpolated DFT, and phase difference approximation respectively, and discuss the window selection for each method. Experiments show tha the window selection primarily depends on the signal-to-noise ratio (SNR). As a result, the optimal window selection for each method and, reversely, the optimal selection of the estimation method for a specific window are discussed as a function of SNR. Consideration on the computational load and the resolution problem is also briefly discussed.

All-in-focus imaging using average filter-based relative focus measure

Abstract Digital images are normally taken by focusing on an object, resulting in defocused background regions. A popular approach to produce an all-in-focus image without defocused regions is to capture several input images at varying focus settings, and then fuse them into an image using offline image processing software. This paper describes an all-in-focus imaging method that can operate on digital cameras. The proposed method consists of an automatic focus-bracketing algorithm that determines at which focuses to capture images and an image-fusion algorithm that computes a high-quality all-in-focus image. While most previous methods use the focus measure calculated independently for each input image, the proposed method calculates the relative focus measure between a pair of input images. We note that a well-focused region in an image shows better contrast, sharpness, and details than the corresponding region that is defocused in another image. Based on the observation that the average filtered version of a well-focused region in an image shows a higher correlation to the corresponding defocused region in another image than the original well-focused version, a new focus measure is proposed. Experimental results of various sample image sequences show the superiority of the proposed measure in terms of both objective and subjective evaluation and the proposed method allows the user to capture all-in-focus images directly on their digital camera without using offline image processing software.

\(\ell _1\) -regularized recursive total least squares based sparse system identification for the error-in-variables

In this paper an

Time delay estimation based on log-sum and lp-norm penalized minor component analysis

\ell _1

Reweighted l1 regularized TLS linear neuron for the sparse system identification

ℓ1-regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed