KEYWORDS: Video, Databases, Internet, Video processing, Distance measurement, Detection and tracking algorithms, System identification, Digital watermarking, Electroluminescence, Document management
Research that began a decade ago in video copy detection has developed into a technology known as "video
fingerprinting". Today, video fingerprinting is an essential and enabling tool adopted by the industry for video content
identification and management in online video distribution. This paper provides a comprehensive review of video
fingerprinting technology and its applications in identifying, tracking, and managing copyrighted content on the Internet.
The review includes a survey on video fingerprinting algorithms and some fundamental design considerations, such as
robustness, discriminability, and compactness. It also discusses fingerprint matching algorithms, including complexity
analysis, and approximation and optimization for fast fingerprint matching. On the application side, it provides an
overview of a number of industry-driven applications that rely on video fingerprinting. Examples are given based on
real-world systems and workflows to demonstrate applications in detecting and managing copyrighted content, and in
monitoring and tracking video distribution on the Internet.
KEYWORDS: Video, Internet, Video processing, Signal processing, Video compression, Computer programming, Error control coding, Forward error correction, Scalable video coding, Video coding
Despite the commercial success, video streaming remains a black art owing to its roots in proprietary commercial development. As such, many challenging technological issues that need to be addressed are not even well understood. The purpose of this paper is to review several important signal processing issues related to video streaming, and put them in the context of a client-server based media streaming architecture on the Internet. Such a context is critical, as we shall see that a number of solutions proposed by signal processing researchers are simply unrealistic for real-world video streaming on the Internet. We identify a family of viable solutions and evaluate their pros and cons. We further identify areas of research that have received less attention and point to the problems to which a better solution is eagerly sought by the industry.
This paper studies the algorithms that reconstruct a signal from its wavelet extrema representation. We show that the existing reconstruction algorithms are inadequate in assuring a consistent reconstruction. We further propose a method that can be used with a number of existing algorithms to guarantee a consistent reconstruction. The new method provides a rigorous way to prevent artifacts resulting from the spurious wavelet extrema in the reconstructed signal.
Blocking artifacts are the most objectionable drawback of block-based image and video coders. We describe a novel technique for removing blocking artifacts via multiscale edge processing. The new technique exploits the advantages of an invertible multiscale edge representation from which the block edges can be easily identified and removed. By virtue of the multiscale edge processing one is able to deblock images effectively without blurring perceptually important features or introducing new artifacts. We present the deblocking algorithm with experimental results and a discussion.
Several methods have been used to obtain complete MR images from a reduced set of measured encodes. All of these techniques use some sort of a priori information to complete the incomplete data set. These techniques have had varying degrees of success in a variety of applications: imaging contrast uptake, interventional procedures, spectroscopy, fMRI contrast changes, and routine scans of a given population. In this paper we make three points: First, significant effort has been made toward finding the set of phase encodes that minimizes the expected L2 norm of the encoding error. We show that in experiments many sets of phase encodes come close to attaining the Karhunen-Loeve (K-L) limit. However, second, we show that other image quality metrics result in much more pleasing images that have much better detail than images where the L2 norm is optimized. For this purpose we use best basis algorithms to find a local trigonometric and wavelet packet bases which optimizes several cost functions including the Lp norms and entropy; the L0.25 norm gave the most pleasing results. Lastly, we show that although ringing produced by undersampling cannot be eliminated it can be effectively controlled when the general shape of the object is known. Ringing renders techniques like SLIM impractical. We are able to control ringing by fitting the reconstructed signal to a piecewise monotonic function. The larger peaks are kept and the fast oscillations characteristic of ringing are eliminated. The signal can be pulled out of noise or fast oscillations when the signal energy is only one quarter of the noise energy.
Image coding is one of the most visible applications of wavelets. There has been an increasing number of reports each year since the late 1980s on the design of new wavelet coders and variations to existing ones. In this paper, we report some results from our comparative study of wavelet image coders using a perception-based, quantitative picture quality scale as the distortion measure. Coders are evaluated in rate-distortion sense; the influences of different wavelets, quantizers, and encoders are assessed individually. Our results provide an insight into the design issues of optimizing wavelet coders, as well as a good reference for application developers to choose from an increasingly large family of wavelet coders for their applications.
Experience suggests the existence of a connection between the contrast of a gray-scale image and the gradient magnitude of intensity edges in the neighborhood where the contrast is measured. This observation motivates the development of edge-based contrast enhancement techniques. We present a simple and effective method for
image contrast enhancement based on the multiscale edge representation of images. The contrast of an image can be enhanced simply by stretching or upscaling the multiscale gradient maxima of the image. This method offers flexibility to selectively enhance features of different sizes and ability to control noise magnification. We present some experimental results from enhancing medical images and discuss the advantages of this wavelet approach over other edge-based techniques.
One of the primary visual properties used by radiologists in classifying masses is the sharpness of the edge of the mass. Wavelet transforms can be thought of as multiscale edge detectors. We report using the edge detection and classification properties of wavelet transforms to help classify masses on mammograms. We digitized six masses from mammograms: three benign and three malignant. Our preliminary results indicate that edge properties of masses in mammograms can be obtained from features in the wavelet transform domain. These edge properties can be used to help classify masses prior to biopsy. In particular, the change in the direction of the edge gradient at intermediate scales is indicative of malignancy. This work must be extended to a much larger sample size. The larger sample size will allow other measures to be used. More importantly the interaction between measures can then be observed. Undoubtedly a combination of measures will be required to classify masses accurately.
We demonstrate a simple and effective method for image contrast enhancement based on the multiscale edge representation of images. The contrast of an image can be enhanced simply by stretching or scaling the multiscale gradient maxima of the image. This method offers flexibility to selectively enhance features of different sizes and ability to control noise magnification. Experimental results from enhanced medical images are presented.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.