35355701
Methods and needs for transcoding MPEG-4 fine granularity scalability video
We discuss the methods and needs for transcoding MPEG-4 fine granularity scalability (FGS) video. FGS is a scalable video-coding technique recently adopted by the MPEG-4 compression standard. The main feature of FGS coding is that a part of the video bit stream, namely the enhancement layer, can be truncated at any desirable number of bits to support continuous quality/bit-rate scalability. However, not every decoder would support FGS functions and the coding efficiency of FGS video is known to be inferior to that of non-scalable video. This paper proposes methods for transcoding FGS video and restoring, as much as possible, the coding efficiency that is lost in FGS coding. In particular, it is shown that there are cases where extra gain of video quality is possible by exploiting the FGS enhancement layer in the transcoding process. To some extend, the proposed transcoding methods can support spatial scalability that is lacking in FGS coding. Experiments have been conducted and the results are reported to demonstrate the advantages of the proposed transcoding schemes for FGS video.
31922506
Layering-based color filter array interpolation
This paper presents a new method to interpolate the color filter array (CFA) pattern that is commonly used in a single-sensor digital camera. The proposed method involves two main steps: slicing the color planes into layers to identify smooth local image regions; estimating the missing color values based on the spectral correlation between color planes. The performance of the proposed algorithm is evaluated by comparing the results with those generated by other existing methods. It shows that the proposed method outperforms other methods in terms of both subjective and objective image quality.
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.
22284885
Motion vector re-estimation for fractional-scale video transcoding
In video transcoding, the compressed video is often converted to a lower resolution in order to reduce the bit rate. One way to achieve this is to first decompress the compressed video, down-sample each frame, and then re-compress the down-scaled frame sequences. This method can obtain high-quality compressed video bit-streams; however, it involves motion vector estimation that is computationally complex. Recently, some transcoding techniques have been proposed to compute the motion vectors for a compressed video with lower resolution using the motion vectors directly from the original compressed video. These techniques, however, cannot be used in arbitrary-size video down-scaling, e.g for converting CIF (Common Intermediate Format) video to sub-QCIF (Quarter CIF) video or QCIF to sub-QCIF. In this paper, we propose a video transcoding method that can estimate the motion vectors for arbitrary-size down-scaled video. It can provide video quality that is comparable to full-search motion estimation but with very low computational complexity.
34798576
Video transcoding for fast forward/reverse video playback
Fast forward and fast reverse playbacks are two common video browsing functions provided in many analog and digital video players. They help users quickly find and access video segments of interest by scanning through the content of a video at a faster than normal playback speed. We propose a video transcoding approach to realizing fast forward and reverse video playbacks by generating a new compressed video from a pre-coded video. To reduce the computational requirements, we design and compare several fast algorithms for estimating the motion vectors required in transcoded video. To accommodate changes due to frame skipping for fast video playback, we also alter the group-of-pictures structure of transcoded video. In addition, subjective tests are conducted to assess the minimum video peak-signal-to-noise-ratio degradation that is perceptible to viewers at different fast playback speeds. To this end, we obtain an adaptive video transcoding method, which combines intra-coding and inter-coding with a fast motion vector reestimation method to strike a good balance between computational complexity and transcoded video quality. Experimental results are reported to show the efficacy of the proposed method.
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.
31919378
A risk management of ASPs
Application service providers (ASPs) provide access to application software over wide area networks and have been viewed as a means of making the benefits of outsourcing available to small businesses. However, many enterprise ASPs get the majority of their revenue from very large businesses while many vertical niche ASPs get their revenue from small businesses. We analyze this seeming anomaly from a risk management perspective. ASPs present a greater risk management problem to would-be customers than traditional outsourcing. We analyze the nature of the risk differences and then propose a model of how would-be customers process those risks. In our model, prospects first assess whether they can tolerate the worst case scenario and if they are able to do due diligence on the likely outcome. They consider benefits only if one of the previous assertions is true. The worst case scenario for enterprise applications is truly dire and only technologically sophisticated businesses are better able to identify the true risk profile. For vertical niche ASPs the worst case scenario is relatively mild, but economies of scale make the benefits relatively more appealing to small businesses.
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.
46672294
Guest editorial
Why model, analyze and build trust in the context of Agent-based systems? : An additional argument. One of the more interesting and relevant characteristics of the agent paradigm is the so-called autonomous behavior of the agent. We can say that almost each deÐnition of agent includes this concept of autonomy ; even if not always in the literature, this characteristic is so well deÐned and completely shared. In particular, an autonomous agent acts without the direct and complete intervention or control of any other agent (either user or software). More precisely, an autonomous agent can independently look for and acquire information, react to local contexts and events, have an evolving internal state, learn or evolve, and decide how and with whom to achieve a task that is not completely speciÐed. In general, when an agent has to collaborate with other agents to obtain its own goals (and this is in fact necessary for software agents that have limited resources and abilities, and operate in open and uncertain worlds), it should allocate (and receive allocation of) tasks to or by other agents. In sum, the characterization of an agent as autonomous and its need for collaboration with other agents to realize its own plans is one of the main reasons for introducing the notion of trust, analyzing its internal components, and adapting it to the various collaborative contexts. In this sense, to build an adequate model of trust would allow an agent (user) to evaluate
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.
181942
Search engine driven author disambiguation
In scholarly digital libraries, author disambiguation is an important task that attributes a scholarly work with specific authors. This is critical when individuals share the same name. We present an approach to this task that analyzes the results of automatically-crafted Web searches. A key observation is that pages from rare Web sites are stronger source of evidence than pages from common Web sites, which we model as inverse host frequency (IHF). Our system is able to achieve an average accuracy of 0.836