Object segmentation is an important preprocessing step for many target
recognition applications. Many segmentation methods have been studied,
but there is still no satisfactory effectiveness measure which makes
it hard to compare different segmentation methods, or even different
parameterizations of a single method. A good segmentation evaluation
method not only would enable different approaches to be compared, but
could also be integrated within the target recognition system to
adaptively select the appropriate granularity of the segmentation
which in turn could improve the recognition accuracy. A few
stand-alone effectiveness measures have been proposed, but these
measures examine different fundamental criteria of the objects, or
examine the same criteria in a different fashion, so they usually work
well in some cases, but poorly in the others. We propose a em
co-evaluation framework, in which different effectiveness measures
judge the performance of the segmentation in different ways, and their
measures are combined by using a machine learning approach which
coalesces the results. Experimental results demonstrate that our
method performs better than the existing methods.
In region-based image segmentation, an image is partitioned into connected regions by grouping neighboring pixels of similar features. To achieve fine-grain segmentation at the pixel level, we must be able to define features on a per-pixel basis. Typically for individual pixels, texture feature extraction is very computationally intensive. In this paper, we propose a new hierarchical method to reduce the computational complexity and expedite texture feature extraction, by taking advantage of the similarities between the neighboring pixels. In our method, an image is divided into blocks of pixels of different granularities at the various levels of the hierarchy. A representative pixel is used to describe the texture within a block. Each pixel within a block gets its texture feature values either by copying the corresponding representative pixel’s texture features, if its features are deemed sufficiently similar, or by computing its own texture features if it is a representative pixel itself. This way, we extract texture features for each pixel in the image with the minimal amount of texture feature extraction computation. The experiments demonstrate the good performance of our method, which can reduce 30% to 60% of the computational time while keeping the distortions in the range of 0.6% to 3.7%. By tailoring the texture feature extraction threshold, we can balance the tradeoff between extraction speed and distortion according to the each system’s specific needs.
The first step towards the design of video processors and video systems is to achieve an accurate understanding of the major video applications, including not only the fundamentals of the many video compression standards, but also the workload characteristics of those applications. Introduced in 1997, the MediaBench benchmark suite provided the first set of full application-level benchmarks for studying video processing characteristics, and has consequently enabled significant research in computer architecture and compiler research for multimedia systems. To expedite the next generation of systems research, the MediaBench Consortium is developing the MediaBench II benchmark suite, incorporating benchmarks from the latest multimedia technologies, and providing both a single composite benchmark suite as well as separate benchmark suites for each area of multimedia. In the area of video, MediaBench II Video includes both the popular mainstream video compression standards, such as Motion-JPEG, H.263, and MPEG-2, and the more recent next-generation standards, including MPEG-4, Motion-JPEG2000, and H.264. This paper introduces MediaBench II Video and provides a comprehensive workload evaluation of its major processing characteristics.
JPEG2000 is the latest still image coding standard. It was designed to overcome the limitations of the original JPEG standard and provide high quality images at low bit rates. The JPEG2000 algorithm is fundamentally based on the Discrete Wavelet Transform (DWT) and Embedded Block Coding with Optimal Truncation (EBCOT). Both of the algorithms are computationally intenstive and require significant memory bandwidth. In this paper we propose a JPEG2000 hardware/software co-processing architecture that complements existing JPEG2000 software packages with efficient dedicated hardware units for EBCOT tier-1 coding, enabling significant speedup.
Accurate image segmentation is important for many image, video and computer vision applications. Over the last few decades, many image segmentation methods have been proposed. However, the results of these segmentation methods are usually evaluated only visually, qualitatively, or indirectly by the effectiveness of the segmentation on the subsequent processing steps. Such methods are either subjective or tied to particular applications. They do not judge the performance of a segmentation method objectively, and cannot be used as a means to compare the performance of different segmentation techniques. A few quantitative evaluation methods have been proposed,
but these early methods have been based entirely on empirical analysis and have no theoretical grounding. In this paper, we propose a novel objective segmentation evaluation method based on information theory. The new method uses entropy as the basis for measuring the uniformity of pixel characteristics (luminance is used in this paper) within a segmentation region. The evaluation method provides a relative quality score that can be used to compare different segmentations of the same image. This method can be used to compare both various parameterizations of one particular segmentation method as well as fundamentally different segmentation techniques. The results from this preliminary study indicate that the proposed evaluation method is superior to the prior quantitative segmentation evaluation techniques, and identify areas for future research in objective segmentation evaluation.
KEYWORDS: Video, Internet, Image quality, Receivers, Video processing, JPEG2000, Video compression, Signal to noise ratio, Associative arrays, Computer programming
Video applications over the Internet are getting increasingly popular because of the explosive growth of the Internet. However, video packets loss due to network congestions can degrade the video quality substantially. In this paper, we propose a transmission scheme for Motion-JPEG2000 video sequences with an active networking approach. Our scheme utilizes the progression modes in Motion-JPEG2000. It can be implemented in an active network environment efficiently. Our simulation shows that the proposed scheme gracefully adapts to network congestion and improves the quality of video transmission in congested IP networks.
KEYWORDS: Video, Video compression, Image quality standards, Image compression, JPEG2000, Image quality, Video coding, Video processing, Wavelet transforms, Standards development
The new ISO/ITU-T standard for still image coding, JPEG2000, has been shown to provide superior coding efficiency to the previous standard, JPEG. Because of the superb performance of JPEG2000, it is reasonable to argue that Motion-JPEG2000, the corresponding moving picture coding standard of JPEG2000, has equally outstanding performance. However, there has not been a sufficient performance evaluation of Motion-JPEG2000. To this end, we have studied the potential of Motion-JPEG2000 for video processing. Our experiments show that Motion-JPEG2000 provides high compression performance, strong error resilience, and good perceptual image quality. Together with a rich set of features inherited from JPEG2000, Motion-JPEG2000 has advantages as a coding standard for video processing in many applications.
This paper presents the results of a quantitative evaluation of the instruction fetch characteristics for media processing. It is commonly known that multimedia applications typically exhibit a significant degree of processing regularity. Prior studies have examined this processing regularity and qualitatively noted that in contrast with general-purpose applications, which tend to retain their data on-chip and stream program instructions in from off-chip, media processing applications are exactly the opposite, retaining their instruction code on-chip and commonly streaming data in from off-chip. This study expounds on this prior work and quantitatively validates their conclusions, while also providing recommendations on architectural methods that can enable more effective and affordable support for instruction fetching in media processing.
KEYWORDS: Multimedia, Video, Video compression, Profiling, Video processing, Image processing, Performance modeling, Standards development, 3D imaging standards, Video coding
As part of our research into programmable media processors, we conducted a multimedia workload characterization study. The tight integration of architecture and compiler in any programmable processor requires evaluation of both technology-driven hardware tradeoffs and application-driven architectural tradeoffs. This study explores the latter area, providing an examination of the application-driven architectural issues from a compiler perspective. Using an augmented version of the MediaBench multimedia benchmark suite, compiling and analysis of the applications are performed using the IMPACT compiler. Characteristics including operation frequencies, basic block and branch statistics, data sizes, working set sizes, and scheduling parallelism are examined for purposes of defining the architectural resources necessary for programmable media processors.
KEYWORDS: Signal processing, Video processing, Computer aided design, Video, Digital signal processing, Video compression, RGB color model, Performance modeling, Clocks, Motion estimation
This paper presents a design methodology for a high- performance, programmable video signal processor (VSP). The proposed design methodology explores both technology-driven hardware tradeoffs and application-driven architectural tradeoffs for optimizing cost and performance within a class of processor architectures. In particular, this methodology allows concurrent consideration of these competing factors at different levels of design sophistication, ranging from early design exploration towards full processor simulation. We present the results of this methodology for an aggressive very-long-instruction-word (VLIW) video signal processor design and discuss its utility for other programmable signal processor designs.
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