In this paper, we propose a wave leader pyramids based Visual Information Fidelity method for image quality assessment. Motivated by the observations that the human vision systems (HVS) are more sensitive to edge and contour regions and that the human visual sensitivity varies with spatial frequency, we first introduce the two-dimensional wavelet leader pyramids to robustly extract the multiscale information of edges. Based on the wavelet leader pyramids, we further propose a visual information fidelity metric to evaluate the quality of images by quantifying the information loss between the original and the distorted images. Experimental results show that our method outperforms many state-of-the-art image quality metrics.
Image interpolation addresses the problem of obtaining high resolution (HR) images from its low resolution (LR) counterparts. For observed LR images with aliasing artifacts caused by undersampling, commonly used interpolation methods cannot recover HR images well, and may often interpolate over-fitting artifacts. In this paper, based on the observation that natural images normally have redundant similar patches, a new patch-synthesis-based interpolation method is proposed for image interpolation. In the proposed method, an inference method based on Markov chain is adopted to select the best patches from the input LR image and synthesize them into the undersampled areas of a desired HR image. In order to improve the efficiency of the algorithm, we also introduce fields of experts to model the sparse prior knowledge and use it to measure the compatibilities among neighboring patches. Experimental results compared with traditional interpolation methods demonstrate that our method cannot only alleviate the aliasing artifact, but also produce better results in terms of quantitative evaluation and subjective visual quality
In this paper, we propose a framework for image classification. An image is represented by multiple feature channels which are computed by the bag-of-words model and organized in a spatial pyramid. The main difference among feature channels resides in what type of base descriptor in the bag-of-words model is extracted. The overall features achieve different levels of the trade-off between discriminative power and invariance. Support vector machines with kernels based on histogram intersection distance and χ2 distance are used to obtain a posteriori probabilities of the image in each feature channel. Then, four data fusion strategies are proposed to combine intermediate results from multiple feature channels. Experimental results show that almost all the proposed strategies can significantly improve the classification accuracy as compared with the single cue methods and, especially, prod-max performs best in all experiments. The framework appears to be general and capable of handling diverse classification problems due to the multiple-feature-channel-based representation. Also, it is demonstrated that the proposed method achieves higher, or comparable, classification accuracies with less computational cost as compared with other multiple cue methods on challenging benchmark datasets.
Proc. SPIE. 5960, Visual Communications and Image Processing 2005
KEYWORDS: Visual process modeling, Detection and tracking algorithms, Error analysis, Computer programming, Video compression, Integration, Mathematics, Error control coding, Stochastic processes, Binary data
CABAC is one of the main entropy coding methods in the H.264 video compression standard. As a binary arithmetic
coding, CABAC can achieve extremely high compression efficiency, but it is sensitive to channel errors. After analyzing
H.264 CABAC framework, an algorithm is proposed to integrate error detection into CABAC. A forbidden symbol is
introduced into the coding alphabet and is allocated a small probability. Whenever this redundant interval is observed in
the decoder output, an error is detected. Mathematical analysis shows that the distribution of error detection rate
approximates geometric probability with the redundancy as its parameter. A small amount of extra redundancy can be
very effective in detecting errors very quickly, and the compression efficiency of CABAC will not be noticeably
undermined. The value of redundancy can easily be adjusted through a single parameter to suit the error characteristics of
the channels in real implementations. Some useful information about the position of the error is also obtained through
this error detection scheme, which can substantially improve the efficiency of succeeding error resilience processing.
Experimental results confirm these conclusions.