nils-herrmann/clean_scirepeval huggingface dataset:
MPEG-21 digital item adaptation: enabling universal multimedia access

The access devices of today are becoming increasingly sophisticated. Thanks to multimedia, communication is much widespread and therefore more powerful. However, we face a serious problem of heterogeneity in our terminals, in our networks, and in the people who ultimately consume and interact with the information presented to them. In this article, we focus on Part 7 of the MPEG-21 standard (ISO/IEC 21000-7), which we refer to as Digital Item Adaptation (DIA). At the time of this writing, the DIA specification is at the penultimate stage of Final Committee Draft (2003); final approval is scheduled for December 2003. The general DIA concept is that Digital Item is subject to both a resource adaptation and a descriptor adaptation engine, which together produce the adapted Digital Item. Note that the standard specifies only the tools that assist with the adaptation process, not the adaptation engines themselves. Digital Item Adaptation (DIA) specifies the following natural environment description tools: location and time and audiovisual environment. The DIA offers a rich set of tools to assist with the adaptation of Digital Items. It offers standardized tools for the description of usage environments, tools to create high-level descriptions of the bitstream syntax to achieve format-independent adaptation, tools that assist in making tradeoffs between feasible adaptation operations and constraints, tools that enable low-complexity adaptation of metadata, and tools for session mobility. Moving forward, the MPEG-21 committee is considering amendments to the specification-for instance, tools that provide further assistance with modality conversion and tools that relate more specifically to the adaptation of audio and graphics media. Furthermore, also being actively considered is how to express the rights that a User has to perform adaptation and how this expression fits into a system that governs those rights.
Keynotes for ISCE 2012

These keynote speeches discuss the following: “A Day Made of Glass” - A vision for an enabled future: challenges and opportunities Presenter/Title: Zachi Baharav, Ph.D. (Researcher, ; 3D Video Coding & Transmission Formats; Evolving Architectures to Support Next Generation Serial Data Rates; Taking your camera from the drawer: Image Sensors Challenges for the Future; Connector Models: Past, Present and Future; Signal Integrity in a Consumer World.
Efficient representation and comparison of multimedia content using DAG-composition

This paper proposes a novel method of representing syntactic and semantic attributes of multimedia content. The methods are based on a new data structure, which includes a directed acyclic graph (DAG) representation. It is well-known that DAGs occupy a middle ground between tree-based and graph-based representations. In the proposed scheme, we exploit the advantages of both representations, as we are able to balance the restrictions on structure and expressiveness of elements with the computational complexity of operations on those elements.
Surveillance System with Object-Aware Video Transcoder

This paper presents an object-aware video surveillance system that is not only smart and friendly for users, but allows for transmission of the scene over limited bandwidth networks. The proposed system stores high-quality video data and associated object metadata, which includes ROI (region-of-interest) information. To satisfy bandwidth constraints and display important parts of the images fast and precisely, the stored images are efficiently transcoded in the compressed-domain based on the ROI information. At the receiver, effective display methods such as a mosaic style allow for an intuitive understanding of the important objects in the scene, as well as their movement over time
Direction-adaptive transforms for coding prediction residuals

In this paper, we present 2-D direction-adaptive transforms for coding prediction residuals of video. These Direction-Adaptive Residual Transforms (DART) are shown to be more effective than the traditional 2-D DCT when coding residual blocks that contain directional features. After presenting the directional transform structures and improvements to their efficiency, we outline how they are used to code both Inter and Intra prediction residuals. For Intra coding, we also demonstrate the relation between the prediction mode and the optimal DART orientation. Experimental results exhibit up to 7% and 9.3% improvements in compression efficiency in JM 16.0 and JM-KTA 2.6r1 respectively, as compared to using only the conventional H.264/AVC transform.
Depth Estimation for View Synthesis in Multiview Video Coding

The compression of multiview video in an end-to-end 3D system is required to reduce the amount of visual information. Since multiple cameras usually have a common field of view, high compression ratios can be achieved if both the temporal and inter-view redundancy are exploited. View synthesis prediction is a new coding tool for multiview video that essentially generates virtual views of a scene using images from neighboring cameras and estimated depth values. In this work, we consider depth estimation for view synthesis in multiview video encoding. We focus on generating smooth and accurate depth maps, which can be efficiently coded. We present several improvements to the reference block-based depth estimation approach and demonstrate that the proposed method of depth estimation is not only efficient for view synthesis prediction, but also produces depth maps that require much fewer bits to code.
Edge map guided adaptive post-filter for blocking and ringing artifacts removal

This paper presents a new adaptive post-filtering algorithm for blocking and ringing artifact reduction. The proposed method extracts local statistics and constructs an edge map of the image. Based on the edge map information, blocking and ringing artifacts are detected, and then reduced by an edge map guided filter. The proposed method specifically targets the blocking artifacts at the block boundaries and ringing artifacts close to edges. Therefore, the algorithm is able to preserve sharp edges and details, while reducing the artifacts. Experimental results demonstrate that our proposed post-filtering algorithm achieves better subjective and objective results compared with other methods. Also, the algorithm has very low complexity.
Online blind deconvolution for sequential through-the-wall-radar-imaging

We propose an online blind deconvolution approach to sequential through-the-wall-radar-imaging (TWI) where the received signal is contaminated by front wall ringing artifacts. The sequential measurements correspond to individual transmitter-receiver pairs where the front wall ringing induces a multipath kernel that corrupts the received target reflections. The convolution kernels may vary across sequential measurements but are assumed to be shared among targets viewed by a single measurement. Our approach extends recent convex programming formulations for blind deconvolution to the sequential measurement scenario by formulating it as a low-rank tensor recovery problem. We develop a stochastic gradient descent algorithm that is capable of recovering the sparse scene and separating out the delay convolution kernels. We demonstrate the recovery capabilities of our approach on a synthetic scene as well as with real TWI radar measurements.
Drift compensation for reduced spatial resolution transcoding

This paper discusses the problem of reduced-resolution transcoding of compressed video bitstreams. An analysis of drift errors is provided to identify the sources of quality degradation when transcoding to a lower spatial resolution. Two types of drift error are considered: a reference picture error, which has been identified in previous works, and error due to the noncommutative property of motion compensation and down-sampling, which is unique to this work. To overcome these sources of error, four novel architectures are presented. One architecture attempts to compensate for the reference picture error in the reduced resolution, while another architecture attempts to do the same in the original resolution. We present a third architecture that attempts to eliminate the second type of drift error and a final architecture that relies on an intrablock refresh method to compensate for all types of errors. In all of these architectures, a variety of macroblock level conversions are required, such as motion vector mapping and texture down-sampling. These conversions are discussed in detail. Another important issue for the transcoder is rate control. This is especially important for the intra-refresh architecture since it must find a balance between number of intrablocks used to compensate for errors and the associated rate-distortion characteristics of the low-resolution signal. The complexity and quality of the architectures are compared. Based on the results, we find that the intra-refresh architecture offers the best tradeoff between quality and complexity and is also the most flexible.
Robust Learning of 2-D Separable Transforms for Next-Generation Video Coding

With the simplicity of its application together with compression efficiency, the Discrete Cosine Transform(DCT) plays a vital role in the development of video compression standards. For next-generation video coding, a new set of 2-D separable transforms has emerged as a candidate to replace the DCT. These separable transforms are learned from residuals of each intra prediction mode, hence termed as Mode dependent-directional transforms (MDDT). MDDT uses the Karhunen-Loeve Transform (KLT) to create sets of separable transforms from training data. Since the residuals after intra prediction have some structural similarities, transforms utilizing these correlations improve coding efficiency. However, the KLT is the optimal approach only if the data has a Gaussian distribution without outliers. Due to the nature of the least-square norm, outliers can arbitrarily affect the directions of the KLT components. In this paper, we will address robust learning of separable transforms by enforcing sparsity on the coefficients of the representations. With this new approach, it is possible to improve upon the video coding performance of H.264/AVC by up to 10.2% BD-rate for intra coding. At no additional cost, the proposed techniques can also provide up to 3.9% improvement in BD-rate for intra coding compared to existing MDDT schemes.
Secure distortion computation among untrusting parties using homomorphic encryption

Alice and Bob possess sequences xn and yn respectively and would like to compute d(xn, yn) where d(· , ·) is a distortion measure. However, Alice and Bob do not trust each other and do not wish to reveal their data to each other. This paper describes and analyzes a protocol that uses homomorphic encryption for secure calculation of some special distortion functions without revealing xn and yn. The resulting distortion result is also in encrypted form. Two variants of the protocol are presented, one for the Hamming distance between binary vectors, and the other for squared error distortion between integer vectors. An application of the protocol for private biometric authentication is described in which Bob interacts with a remote encrypted fingerprint database (Alice) to achieve access control without revealing his own identity.
Error resilience video transcoding for wireless communications

Video communication through wireless channels is still a challenging problem due to the limitations in bandwidth and the presence of channel errors. Since many video sources are originally coded at a high rate and without considering the different channel conditions that may be encountered later, a means to repurpose this content for delivery over a dynamic wireless channel is needed. Transcoding is typically used to reduce the rate and change the format of the originally encoded video source to match network conditions and terminal capabilities. Given the existence of channel errors that can easily corrupt video quality, there is also the need to make the bitstream more resilient to transmission errors. In this article we provide an overview of the error resilience tools found in today's video coding standards and describe a variety of techniques that may be used to achieve error-resilient video transcoding.
Adaptive fuzzy post-filtering for highly compressed video

This paper presents a newly developed adaptive fuzzy post-filter for removing blocking and ringing artifacts in the highly compressed images and videos. The proposed post-filter adoptively adjusts its filtering window size and the spread parameter in the fuzzy membership function according to the variance value in an edge map. The post-filtering operation is directly applied to the classified edge blocks in the image under the guidance of the edge map, while keeping smooth and textured blocks unchanged. The experimental results demonstrate that the proposed method possesses a good edge preserving property and can maximally remove coding artifacts. Compared with our previous method, the proposed post-filter achieves better objective and subjective quality and further reduces computational complexity.
Fast adaptive fuzzy post-filtering for coding artifacts removal in interlaced video

The paper presents a new method using fuzzy filtering to remove the coding artifacts in compressed video. The method takes the interlaced video format into consideration and processes each field separately. For deblocking, a 1D fuzzy filter with different window sizes is used to remove the horizontal and vertical blocking artifacts respectively. For deringing, each 8/spl times/8 block in a field is first classified into one of the four categories, i.e., strong edge, weak edge, texture and smooth blocks. According to each block's type and the neighboring block's type, the spread parameter of a 2D fuzzy filter is adaptively decided and the filter is applied To speed up the process, the fuzzy filter weights are generated using a piecewise linear membership function instead of the conventional Gaussian function. Experimental results show that the proposed method has better detail preservation and lower computational costs than our previous method. It achieves comparable deblocking and superior deringing performance to the MPEG-4 standard method at similar computation costs.
Coding artifacts reduction using edge map guided adaptive and fuzzy filtering

The paper presents a new adaptive approach for blocking and ringing artifact reduction. In order to avoid smearing of the image details, the proposed method first performs visual artifact detection and then applies adaptive filtering to the corrupted blocks. Both visual artifact detection and filtering are guided by an edge map which is constructed based on local features. A fuzzy identity filter is used for image de-ringing. Since it possesses a good edge preserving property and the filtering operation is applied to the edge blocks only (smooth and textured blocks are unaltered), the proposed method shows great effectiveness of both artifact reduction and detail preservation. Experiments demonstrate better results compared with other methods
A New Approach For Memory Efficient ATV Decoding

In this paper we propose an efficient memory saving architecture for an ATV decoder. In this new architecture, the motion-compensation is performed in the frequency domain. This makes it possible for anchor frames to be saved as compressed bits, resulting in significant memory savings with the addition of several extra devices in the decoder. The increase in complexity is small compared to the reduction in memory requirements. The simulation results with the new decoder architecture have shown that the reconstructed image quality is close to that of the conventional decoder.
Encoding and transcoding multiple video objects with variable temporal resolution

A key advantage of object-based coding schemes is that the quality of each video object may be varied. In this paper we describe the problem associated with varying the temporal quality of multiple video objects, also referred to as the composition problem. We propose a solution to this problem that is based on changes in the shape boundaries over time. Finally, we discuss how this shape feature, or shape hint, can be used to vary the temporal resolution of multiple video objects. The use of the proposed shape hint may be considered within an encoding or transcoding application. This hint has also been adopted into the emerging MPEG-7 standard.
Complexity and memory efficient GOP structures supporting VCR functionalities in H.264/AVC

Supporting digital video cassette recording (VCR) trick-play functionalities (e.g. random access, fast-forward play, fast-reverse play) is desirable for compressed video streams. However, due to strong inter-frame dependencies introduced by motion compensated prediction (MCP), the computational complexity and memory requirement is drastically increased. Tradeoffs between coding efficiency and decoding complexity can be made with different group of pictures (GOP) structures. In this paper, we investigate two flexible GOP structures, named G-group and binary reference GOP structure (BRGS), which can achieve trick-play functionalities while keeping low decoder complexity and memory requirement for the state-of-the-art H.264/AVC video coding standard. The schemes are drift-free since they utilize the compression and memory management tools adopted in H.264/AVC. Our analysis and experimental results show that they can greatly reduce the decoder complexity and buffer size while introducing only about 4.0%-7.6% bitrate increase on average. The computational complexity saving for the worst case and average case can be up to 77.8% and 62.5%, respectively. The memory buffer for the fast-reverse play mode can be reduced to 33.3% compared to the conventional scheme. Moreover, the schemes are flexible and can be easily adapted to achieve a good tradeoff between compression performance and complexity saving for the trick-play modes.
Consistency and convexity in object reconstruction from projections

A projection-space approach for reconstruction from projections is presented. This approach uses the known convex support of the object both as a penalty term in a variational problem defined in projection-space and as a guide to the specification of optimal smoothing coefficients that may vary spatially. Partial consistency of the sinogram is provided by including mass and center-of-mass constraints in the variational formulation. An outline of the general approach and calculations for a specific example are provided. Computer simulations are provided for evaluation of the performance in this special case
Intensity inhomogeneity correction of macular OCT using N3 and retinal flatspace

As optical coherence tomography (OCT) has increasingly become a standard modality for imaging the retina, automated algorithms for processing OCT data have become necessary to do large scale studies looking for changes in specific layers. To provide accurate results, many of these algorithms rely on the consistency of layer intensities within a scan. Unfortunately, OCT data often exhibits inhomogeneity in a given layer's intensities, both within and between images. This problem negatively affects the performance of segmentation algorithms and little prior work has been done to correct this data. In this work, we adapt the N3 framework for intensity inhomogeneity correction, which was originally developed to correct MRI data, to work for macular OCT data. We first transform the data to a flattened macular space to create a template intensity profile for each layer giving us an accurate initial estimate of the gain field. N3 will then produce a smoothly varying field to correct the data. We show that our method is able to both accurately recover synthetically generated gain fields and improves the stability of the layer intensities.
A multiple geometric deformable model framework for homeomorphic 3D medical image segmentation

This paper presents a 3D segmentation framework for multiple objects or compartments embedded as level sets. Thanks to a compact representation of the level set functions of multiple objects, the framework guarantees no overlap and vacuum, and leads to a computationally efficient evolution scheme largely independent of the number of objects. Appropriate topology constraints ensure not only that the topology of each object remains the same, but that the relationship between objects is also maintained. The decomposition of objects makes the framework specifically attractive to the segmentation of related anatomical regions or the parcellation of an organ, where relationships must be maintained and different evolution forces are needed on different parts of the objects interface. Examples of 3D whole brain segmentation and thalamic parcellation demonstrate the potential of our method for such segmentation tasks.
Stochastic formulations of optical flow algorithms under variable brightness conditions

In this paper, we examine optical flow methods applicable under variable brightness conditions. In particular, we develop a stochastic formulation of Gennert and Negahdaripour's (1987) optical flow algorithm. Our model is shown to be well-posed and thus can be used in both simulating and estimating velocity fields with certain known stochastic properties. This model is used to simulate flow fields of different physical properties, and Gennert and Negahdaripour's method is demonstrated to work well for these synthesized fields under variable brightness conditions.
A fiber tracking method guided by volumetric tract segmentation

This paper presents a fiber tracking method that solves fiber crossing and avoids anatomically improbable fiber tracts using clinical-quality diffusion tensor images. The method tracks fiber pathways under the guidance of global information from volumetric tract segmentation obtained from the algorithm of diffusion oriented tract segmentation (DOTS). New primary diffusion directions (PDD) are calculated in crossing areas labeled by DOTS, by solving the Euler-Lagrange equation. We replace the PDD field obtained from the tensors with the new PDD field, which we refer to as DOTS-PDD, in crossing areas in tractography. Results on synthetic data and real data demonstrate that the proposed method manages to propagate fiber pathways through crossing area and produce anatomically desirable fibers.
Partial volume estimation and the fuzzy C-means algorithm [brain MRI application]

Partial volume averaging (PVA) is present in nearly all practical imaging situations, medical imaging in particular. One method that has been used to account for the effects of PVA is the fuzzy c-means algorithm (FCM). The authors propose a new method for estimating the partial volume coefficient of each class at each voxel in a given image using a Bayesian statistical model. A prior probability on the partial volume coefficients is used to reject how most voxels in the image are expected to be pure. The authors then show that the results obtained by this method are quite similar and in some cases equivalent to results obtained using FCM. Both algorithms are demonstrated on a magnetic resonance image of the brain.
On the relationship between parametric and geometric active contours

Geometric active contours have many advantages over parametric active contours, such as computational simplicity and the ability to change the curve topology during deformation. While many of the capabilities of the older parametric active contours have been reproduced in geometric active contours, the relationship between the two has not always been clear. We develop a precise relationship between the two which includes spatially-varying coefficients, both tension and rigidity, and non-conservative external forces. The result is a very general geometric active contour formulation for which the intuitive design principles of parametric active contours can be applied. We demonstrate several novel applications in a series of simulations.