본 논문에서는 비디오 화질 평가를 위해 움직임 벡터의 1차원 히스토그램을 비디오의 특징으로 이용하는 새로운 reduced-reference (RR) 평가 방법을 제안하였다. 제안한 화질 평가 방법은 수신단에서 열화 비디오를 재구성하는 대신 비디오 스트림 (video stream)의 파싱 (parsing) 과정에서 움직임 벡터를 직접 얻을 수 있기 때문에 수행시간 면에서 기존의 방법들에 비해 효율적이다. 또한 송신단에서는 입력 비디오 영상 전체에 대해 누적된 움직임 벡터의 1차원 히스토그램을 보내기 때문에 데이터량 측면에서도 효율적이다. 여기서, 기존의 방법들이 영상 한 장씩에 대해서 평가를 했던 것과 달리 제안한 방법에서는 전체 영상에 대해 누적된 움직임 벡터의 1차원 히스토그램을 사용하였다. 히스토그램의 유사도를 측정하기 위해 히스토그램 인터섹션 (histogram intersection)과 히스토그램 차이 (histogram difference)을 사용하였다. 여러 가지 비트율 (bit rate), 영상크기, 프레임율 (frame rate)로 코딩된 비디오 클립 52개에 대해 제안한 방법과 기존의 방법들을 비교하였고, 제안한 방법의 효율성을 기존 방법들과의 비교 실험을 통해 보였으며, 실험 결과를 통해, 제안한 방법이 기존의 방법들보다 mean opinion score (MOS)와 유사함을 보였다.
Channel equalization techniques for full-digital high definition television (HDTV) systems are investigated. Conventional equalization methods are surveyed and a variable step size least mean square (VS-LMS) algorithm using the simple time constant concept is proposed. Several equalization techniques for HDTV systems are simulated for various channel models, and their characteristics are analyzed. Also the simulation results of the equalizer using fixed-point operations are shown.< >
A Hausdorff distance (HD) is one of commonly used measures for object matching. This work analyzes the conventional HD measures and proposes two robust HD measures based on m-estimation and least trimmed square (LTS) which are more efficient than the conventional HD measures. By computer simulation, the matching performance of the conventional and proposed HD measures is compared with synthetic and real images.
A method for computing the effective bandwidth of aggregated variable bit rate (VBR) Moving Picture Experts Group (MPEG) video traffic is proposed. First, individual MPEG traffic are split into I, B, and P frame traffic according to the frame type and the respective I, B, and P frame traffic are aggregated, where transform expand sample (TES) processes are employed for modeling the MPEG traffic. Second, we compute the statistical characteristics of the aggregated I frame traffic, aggregated B frame traffic, and aggregated P frame traffic using the individual I, B, and P frame traffic, where the statistical characteristics represent the mean, second and third central moments, and the lag 1 autocorrelation of the bit rate of the traffic. Next, the effective bandwidth of the aggregated I frame traffic is computed by the Gaussian bound. We calculate the statistical characteristics of the combined B and P frame traffic using those of the aggregated B frame and P frame traffic, and estimate the effective bandwidth of the combined B and P frame traffic using the modified equivalent capacity. Finally, we compute the total effective bandwidth of the aggregated VBR MPEG traffic by adding the Gaussian bound of the aggregated I frame traffic and the modified equivalent capacity of the combined B and P frame traffic. Computer simulation shows that the proposed method provides a good estimate of the total effective bandwidth of the aggregated VBR MPEG traffic.
Histogram equalization (HE), a simple contrast enhancement (CE) method, tends to show excessive enhancement and gives unnatural artifacts on images with high peaks in their histograms. Histogram-based CE methods have been proposed in order to overcome the drawback of HE, however, they do not always give good enhancement results. In this letter, a histogram-based locality-preserving CE method is proposed. The proposed method is formulated as an optimization problem to preserve localities of the histogram for performing image CE. The locality-preserving property makes the histogram shape of the enhanced image to be similar to that of the original image. Experimental results show that the proposed histogram-based method gives output images with graceful CE on which existing methods give unnatural results.
This study proposes an unsupervised binary hashing (UBH) method and provides a comprehensive analysis and evaluation to the UBH method in image similarity. Using the orthogonal locality preserving projection, the proposed UBH method performs the dimensionality reduction (DR). To reduce the quantisation error between low‐dimensional vectors and binary hash codes generated in the DR, the proposed UBH method calculates the optimal parameters of rotation and offset. The authors use two test beds to evaluate the preservation of original feature space and the semantic consistency. Experimental results with two test beds show that the proposed UBH method is state of the art.
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