16126034
Color filter array demosaicking: new method and performance measures
Single-sensor digital cameras capture imagery by covering the sensor surface with a color filter array (CFA) such that each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to as CFA demosaicking, is required to estimate the other two missing color values at each pixel. In this paper, we present two contributions to the CFA demosaicking: a new and improved CFA demosaicking method for producing high quality color images and new image measures for quantifying the performance of demosaicking methods. The proposed demosaicking method consists of two successive steps: an interpolation step that estimates missing color values by exploiting spatial and spectral correlations among neighboring pixels, and a post-processing step that suppresses noticeable demosaicking artifacts by adaptive median filtering. Moreover, in recognition of the limitations of current image measures, we propose two types of image measures to quantify the performance of different demosaicking methods; the first type evaluates the fidelity of demosaicked images by computing the peak signal-to-noise ratio and CIELAB DeltaE(*)(ab) for edge and smooth regions separately, and the second type accounts for one major demosaicking artifact-zipper effect. We gauge the proposed demosaicking method and image measures using several existing methods as benchmarks, and demonstrate their efficacy using a variety of test images.
21500667
Efficient block-matching motion estimation based on Integral frame attributes
Block-based motion estimation is widely used in video compression for exploiting video temporal redundancy. Although effective, the process is arguably the most computationally intensive part of a typical video encoder. To speed up the process, a large number of fast block-matching algorithms (BMAs) have been proposed for motion estimation by limiting the number of search locations or simplifying the measure of match between the two blocks under comparison. In this paper we propose a new BMA that measures the match by using such features as block sum and block variance, which can be easily computed using integral frame attributes. Experimental results show that the proposed BMA can reduce the computational complexity notably and achieve compression performance very close to that of existing BMAs using conventional block-matching measures.
27936359
A new content-based hybrid video transcoding method
Many video transcoding architectures have been proposed to reduce the bitrates of compressed video bitstreams. Requantization of transformed coefficients (such as DCT coefficients), spatial resolution down-sampling, and temporal resolution down-sampling are the three transcoding tools which are commonly used to reduce compressed video bitrates. We propose a new video transcoding method that combines the use of different transcoding tools to transcode compressed video bit streams to various target bitrates while maintaining good subjective/objective video quality. The transcoding tools are selected based on the video content, which is characterized by two video content descriptors inferring the video spatial and motion activities, respectively. These two content descriptors can be readily computed from the compressed video bit streams without full decompression. Experimental results show that the proposed method can provide better video quality than other existing transcoders that make use of a single transcoding tool.
32434128
Fast motion vector and bitrate re-estimation for arbitrary downsizing video transcoding
In video transcoding technique, many fast methods have been proposed to estimate the motion vectors required for downsizing a pre-coded video by an integer factor. In this paper we examine and compare several fast video transcoding methods for downsizing a pre-coded video by an arbitrary factor. Methods which out-perform others are identified and discussed. We also propose a new method to model the relation between bitrate and frame size under low bitrate condition. With this new model, we can re-estimate the bitrate according to a required frame size or select a suitable frame size for a given target bitrate, while maintaining good video quality. Experimental results are presented to show the performance of the proposed fast motion vector and bitrate re-estimation methods.
33005423
Unsupervised clustering of dominant scenes in sports video
We propose a new and efficient approach for clustering dominant scenes in sports video. To perform clustering in an unsupervised manner, we devise a recursive peer-group filtering (PGF) scheme to identify prototypical shots for each dominant scene, and examine time coverage of these prototypical shots to decide the number of dominant scenes for each sports video under analysis. To improve clustering efficiency, we employ principal component analysis and linear discriminant analysis to project high dimensional shot features to lower dimensional spaces suitable for classification. The main contribution of the paper lies in the formulation of clustering dominant scenes in sports video and the development of an efficient, unsupervised solution making use of PGF, time-coverage criterion, and subspace analysis. Experimental results obtained from various types of sports videos are presented to show the efficacy of the proposed approach.
27733167
An unsupervised approach to dominant video scene clustering
In this paper, we propose an unsupervised approach for dominant scene clustering in sports video. By adopting a customized peer group filtering (PGF) to identify prototypes for k-means clustering, dominant scenes can be clustered based on shot color histogram (SCH). Meanwhile, the number of clusters can automatically be determined by estimating the time coverage of dominant scenes. To improve the computational efficiency and clustering accuracy, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to project SCH into reduced dimensional spaces. The prototypes obtained by PGF can also be served as sufficient and representative training data for LDA. Such good training data ensures LDA outperforms PCA with better clustering performance in more reduced feature dimension.
18127609
A new method for camera motion parameter estimation
We derive a six parameter system to estimate and compensate the effects of camera motion-zoom, pan, tilt and swing. As compared to other existing methods, this model describes more precisely the effect of different kinds of camera motions. A recursive least-squares estimator has been used to solve for the motion parameters. Experiments suggest that our algorithm converges to satisfactory results when about 10 pairs of corresponding pairs between two image frames are available.