nils-herrmann/clean_scirepeval huggingface dataset:
VBR video delivery under constrained resources using motion-aware optimal frame selection

This paper proposes an optimal frame selection scheme for motion-aware VBR video delivery under both channel and buffer size constraints. Our objective is to find a feasible set of frames that can maximize the video's accumulated motion metrics without violating any constraint. We use a discrete-time model to monitor client buffer occupancy status at each frame and find the optimal frame selection path with a Viterbi based algorithm. By eliminating non-optimal frame states and considering inter-frame dependencies, our proposed scheme achieves a low computational cost of O(TN), where T is a parameter related to the client buffer size B and N is the total number of frames. The experimental results show that T can be several magnitudes smaller than B. In addition, we investigate the impact of startup latency and propose a way to find the optimal preload
Content-Based Image Compression for Arbitrary-Resolution Display Devices

The existing image coding methods cannot support content-based spatial scalability with high compression. In mobile multimedia communications, image retargeting is generally required at the user end. However, content-based image retargeting (e.g., seam carving) is with high computational complexity and is not suitable for mobile devices with limited computing power. The work presented in this paper addresses the increasing demand of visual signal delivery to terminals with arbitrary resolutions, without heavy computational burden to the receiving end. In this paper, the principle of seam carving is incorporated into a wavelet codec (i.e., SPIHT ). For each input image, block-based seam energy map is generated in the pixel domain. In the meantime, multilevel discrete wavelet transform (DWT) is performed. Different from the conventional wavelet-based coding schemes, DWT coefficients here are grouped and encoded according to the resultant seam energy map. The bitstream is then transmitted in energy descending order. At the decoder side, the end user has the ultimate choice for the spatial scalability without the need to examine the visual content; an image with arbitrary aspect ratio can be reconstructed in a content-aware manner based upon the side information of the seam energy map. Experimental results show that, for the end users, the received images with an arbitrary resolution preserve important content while achieving high coding efficiency for transmission.
Joint Unequal Loss Protection and LT Coding for Layer-Coded Media Delivery

Rateless codes such as LT codes have become more and more popular due to their abilities to handle varying channel conditions without much feedback. However, rateless codes have the drawback of unable to provide intermediate outputs when delivering layer-coded media content. Although some methods have been proposed to produce intermediate outputs based on adjusting the distribution of LT codes, they are typically content-dependent and unable to guarantee that a more important layer can always be decoded before the decoding of a less important layer. In this paper, we propose a simple joint unequal loss protection (ULP) and LT coding (ULP-LT) scheme for layered media delivery, where different numbers of FEC are allocated to different layers to guarantee the priority and LT codes are used to deal with varying channel conditions. Simulation results show that with a small amount of overhead allocated to ULP, the ULP-LT scheme can produce good intermediate performance while still enjoying the nice features provided by LT codes.
Error resilient image coding with rate-compatible punctured convolutional codes

In this paper, we describe an application of the rate-compatible punctured convolutional (RCPC) channel codes to an error-resilient image compression scheme with variable-coefficient fixed-length (VCFL) source codes. The VCFL source coder is error resilient because of its fixed length of the codes. Since it is based on wavelet transform, the scheme provides us with a hierarchical structure of the source codes to facilitate the unequal error protection by the channel codes. We adopt the RCPC codes for channel coding to achieve three goals: (1) unequal error protection, (2) single channel decoder with multiple rate compatibility, (3) minimizing controlled overhead redundancy. Preliminary results show that, with slightly higher bit rate, we can achieve better reconstructed image quality under at least an order of magnitude higher channel bit error rate.
Kinect Shadow Detection and Classification

Kinect depth maps often contain missing data, or "holes", for various reasons. Most existing Kinect-related research treat these holes as artifacts and try to minimize them as much as possible. In this paper, we advocate a totally different idea - turning Kinect holes into useful information. In particular, we are interested in the unique type of holes that are caused by occlusion of the Kinect's structured light, resulting in shadows and loss of depth acquisition. We propose a robust detection scheme to detect and classify different types of shadows based on their distinct local shadow patterns as determined from geometric analysis, without assumption on object geometry. Experimental results demonstrate that the proposed scheme can achieve very accurate shadow detection. We also demonstrate the usefulness of the extracted shadow information by successfully applying it for automatic foreground segmentation.
Estimating spatial layout of rooms from RGB-D videos

Spatial layout estimation of indoor rooms plays an important role in many visual analysis applications such as robotics and human-computer interaction. While many methods have been proposed for recovering spatial layout of rooms in recent years, their performance is still far from satisfactory due to high occlusion caused by the presence of objects that clutter the scene. In this paper, we propose a new approach to estimate the spatial layout of rooms from RGB-D videos. Unlike most existing methods which estimate the layout from still images, RGB-D videos provide more spatial-temporal and depth information, which are helpful to improve the estimation performance because more contextual information can be exploited in RGB-D videos. Given a RGB-D video, we first estimate the spatial layout of the scene in each single frame and compute the camera trajectory using the simultaneous localization and mapping (SLAM) algorithm. Then, the estimated spatial layouts of different frames are integrated to infer temporally consistent layouts of the room throughout the whole video. Our method is evaluated on the NYU RGB-D dataset, and the experimental results show the efficacy of the proposed approach.
An MPEG-4-compatible stereoscopic/multiview video coding scheme

In this paper, we propose an efficient codec for multiview video coding, which is compatible with the MPEG-4 video standard. The main views of the multiview video are encoded using an MPEG-4 encoder and the auxiliary views are encoded by joint disparity and motion compensation. An edge-preserving regularization scheme that jointly calculates disparity and motion vectors is performed on the VOP basis. The output of the encoder contains one bitstream for each view, and the main view bitstreams can be decoded by a standard MPEG-4 decoder. In addition, in the case of five-view encoding, we compare four different prediction structures in order to find the best one under certain scenarios. To evaluate the proposed encoder, the MPEG-2 multiview profile (MVP) is implemented on the MPEG-4 platform for fair comparison, which is referred to as MPEG-4 MVP in this paper. Experimental results prove that the proposed encoder achieves a higher image quality at similar bit rate than the conventional scheme and is very promising for the applications including videoconferencing and three-dimensional telepresence.
Optimized Cross-Layer Design for Scalable Video Transmission Over the IEEE 802.11e Networks

A cross-layer design for optimizing 3-D wavelet scalable video transmission over the IEEE 802.11e networks is proposed. A thorough study on the behavior of the IEEE 802.11e protocol is conducted. Based on our findings, all timescales rate control is developed featuring a unique property of soft capacity support for multimedia delivery. The design consists of a macro timescale and a micro timescale rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets from video classifications to appropriate network priorities which preemptively drops less important video packets to maximize the transmission protection to the important video packets. The performance is investigated by simulations highlighting advantages of our cross-layer design.
Load-sharing in wireless multi-homed systems

In the field of mobile communications, there coexist various access technologies that support wireless connections, such as Bluetooth, IEEE 802.11 and GPRS. These technologies are diverse in terms of bandwidth, delay, coverage area, etc. It is not uncommon that a mobile device is equipped with multiple wireless network interfaces to achieve efficient and ubiquitous communications. In wireless overlay networks, the mobile node can even utilize multiple network interfaces simultaneously to achieve greater QoS performance. This paper presents the design of such a system that supports multi-homed communications. In particular, we design the policy for mapping different network flows to different interfaces, and propose a load-sharing algorithm based on weighted-round-robin channel selection and jump-ahead packet scheduling. Simulation results show that the algorithm can efficiently distribute data packets among multiple channels to achieve bandwidth aggregation.
vCache: Supporting Cost-Efficient Adaptive Bitrate Streaming

In adaptive bitrate (ABR) streaming, each video must be transcoded into multiple representations. Transcoding and caching videos consume tremendous resources, and only a small percentage of video chunks are frequently requested. Thus a question arises: is it necessary to pre-transcode each video and cache all video chunks? To answer this, the authors designed a network function virtualization (NFV)-based virtual cache (vCache). In vCache, video chunks have two mutually exclusive caching states: physically cached and virtually cached. A physically cached video chunk can be directly read from storage, and it consumes storage resources. A virtually cached video chunk will be transcoded online when being requested, and it consumes computing resources. With NFV, vCache can dynamically manage video chunks cost-effectively and can intelligently provision resources to guarantee transcoding delays won't affect streaming services. Results from experiments show that vCache can greatly reduce operational costs for ABR. This article is part of a special issue on advancing multimedia distribution.
Scalable data dissemination protocol for wireless sensor networks

Opportunistic wireless transmission is a technique offering collaboration of simultaneous flows to achieve error resilient transmissions. In this paper, we introduce the use of opportunistic wireless transmission for data diffusion which deals with the all-to-all data dissemination in a wireless sensor network. Our design turns the network infrastructure into a single fountain encoder where source packets arrive from each sensor node and these packets are encoded while flooding throughout the entire network to all nodes. Simulation and analysis are carried out showing that our proposed opportunistic data diffusion achieves efficient and error resilient all-to-all data dissemination. The results confirm that our solution offers not only efficient and reliable all-to-all data dissemination over lossy wireless links, but also scalability to large networks.
Part propagation for local part segmentation

Segment propagation transfers object priors among images, which is an important prior generation manner in image segmentation. The existing propagation methods focus on object foreground propagation, while the detailed part propagation is deficiency, which is caused by the challenges that not only the multiple part regions, but also their relationships need to be transferred. In this paper, a part propagation method is proposed. Two level propagations such as object level propagation, and part level propagation are successively used for the part propagation. The object level propagation is to transfer global shape information among images, which is formulated as graph matching based edge fragments matching problem, with dynamic programming solution. The part level propagation is to transfer the more detailed part labels, which is formulated as pixel level structure matching problem, and is efficiently solved by traditional dense pixel matching methods. The proposed method is verified on 15 challenging classes selected from PASCAL 2010 dataset, Bird dataset and Cat-Dog dataset. The experimental results demonstrate the effectiveness of the proposed method.
Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition

Most existing feature learning-based methods for RGB-D object recognition either combine RGB and depth data in an undifferentiated manner from the outset, or learn features from color and depth separately, which do not adequately exploit different characteristics of the two modalities or utilize the shared relationship between the modalities. In this paper, we propose a general CNN-based multi-modal learning framework for RGB-D object recognition. We first construct deep CNN layers for color and depth separately, which are then connected with a carefully designed multi-modal layer. This layer is designed to not only discover the most discriminative features for each modality, but is also able to harness the complementary relationship between the two modalities. The results of the multi-modal layer are back-propagated to update parameters of the CNN layers, and the multi-modal feature learning and the back-propagation are iteratively performed until convergence. Experimental results on two widely used RGB-D object datasets show that our method for general multi-modal learning achieves comparable performance to state-of-the-art methods specifically designed for RGB-D data.
Exploit Bounding Box Annotations for Multi-Label Object Recognition

Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that contain multiple objects from different categories, scales and locations, global CNN features are not optimal. In this paper, we incorporate local information to enhance the feature discriminative power. In particular, we first extract object proposals from each image. With each image treated as a bag and object proposals extracted from it treated as instances, we transform the multi-label recognition problem into a multi-class multi-instance learning problem. Then, in addition to extracting the typical CNN feature representation from each proposal, we propose to make use of ground-truth bounding box annotations (strong labels) to add another level of local information by using nearest-neighbor relationships of local regions to form a multi-view pipeline. The proposed multi-view multiinstance framework utilizes both weak and strong labels effectively, and more importantly it has the generalization ability to even boost the performance of unseen categories by partial strong labels from other categories. Our framework is extensively compared with state-of-the-art handcrafted feature based methods and CNN based methods on two multi-label benchmark datasets. The experimental results validate the discriminative power and the generalization ability of the proposed framework. With strong labels, our framework is able to achieve state-of-the-art results in both datasets.
Adaptive configuration of cloud video transcoding

Cloud computing is emerging as a new paradigm which enables big data computing, including high quality media processing. However, considering the media dynamics on resource consumption and the QoS criteria, dynamically providing the cloud computing resource to meet the QoS requirements of media processing is not easy. The current cloud computing infrastructure usually employs auto-scaling to dynamically adjust the computing resource allocation, which is typically performed at relatively long time scale and cannot adapt to the dynamic changes of video arrivals or content changes at relatively short time scale. In this paper, we propose to adaptively configure the video transcoding mode to deal with the short-term transcoding QoS and computing resource mismatch problem. We formulate the problem as the one to minimize the output bit-rate with the queue stability constraint, for which we use the Lyapunov optimization framework to solve it. Simulation results show that, compared with the static configuration strategy, the proposed adaptive method achieves smooth transcoding QoS degradation when system load becomes heavier and much better transcoding delay performance.
Improving WLAN VoIP Capacity Through Service Differentiation

Voice over Internet protocol (VoIP) is one of the most important applications for IEEE 802.11 wireless local area networks (WLANs). For network planners who deploy VoIP over WLANs, one of the important issues is the VoIP capacity. Directly implementing VoIP over infrastructure WLANs will create the bottleneck problem at the access point (AP). In this paper, we propose the use of the service differentiation provided by the new IEEE 802.11e standard to solve the bottleneck problem and improve voice capacity. In particular, we propose the allocation of a higher priority access category (AC) to the AP while allocating lower priority AC to mobile stations. We develop a simple Markov chain model, which considers the important enhanced distributed channel access (EDCA) parameters and the channel errors under saturation and nonsaturation conditions. Based on the developed analytical model, we analyze the performance of VoIP over EDCA. By appropriately selecting the EDCA parameters, we are able to differentiate the services for the downlink and uplink. The experimental results are very promising. With the adjustment of only one EDCA parameter, we improve the VoIP capacity by 20%-30%.
Subjective Image Quality Assessment at the Threshold Level

A subjective method for assessing image quality at the threshold level is presented. The assessment methodology has a trichotomous (3-way) structure. It looks at some problems associated with subjective assessments, particularly fatigue and scanning effects. In addition, it addresses the elusive issue of the minimum sample size necessary to achieve statistical relevance in subjective tests. This leads to the question of the reliability of subjects, a problem which may be resolved with a robust assessment method.
Collision Codes: Decoding Superimposed BPSK Modulated Wireless Transmissions

The introduction of physical layer network coding gives rise to the concept of turning a collision of transmissions on a wireless channel useful. In the idea of physical layer network coding, two synchronized simultaneous packet transmissions are carefully encoded such that the superimposed transmission can be decoded to produce a packet which is identical to the bitwise binary sum of the two transmitted packets. This paper explores the decoding of superimposed transmission resulted by multiple synchronized simultaneous transmissions. We devise a coding scheme that achieves the identification of individual transmission from the synchronized superimposed transmission. A mathematical proof for the existence of such a coding scheme is given.
Registration of multiple RGBD cameras via local rigid transformations

RGBD cameras, such as the Kinect, have recently revolutionized the field of real-time geometry and appearance acquisition. While impressive 3D reconstruction results have been obtained, combining data acquired by multiple RGBD cameras constitutes a technical challenge. Several methods have been proposed to estimate the internal parameters of each RGBD camera (such as depth mapping function and focal length). Despite that the textured geometry obtained by each RGBD camera individually is visually attractive, even state-of-the-art methods have difficulties in correctly combining the textured geometries obtained by several RGBD cameras via a rigid transformation. Based on this observation, our approach registers the RGBD cameras by a smooth field of rigid transformations, instead of a single rigid transformation. Experimental results on challenging data demonstrate the validity of the proposed approach.
NEDA: a new distributed arithmetic architecture and its application to one dimensional discrete cosine transform

Conventional Distributed Arithmetic (DA) is popular in ASIC design and it features on-chip ROM to achieve high speed and regularity. In this paper, a new DA architecture called NEDA is proposed aimed at reducing the cost metrics of power and area while maintaining high speed and accuracy in Digital Signal Processing (DSP) applications. Mathematical analysis proves that NEDA can implement inner product of vectors in the form of 2's complement numbers using only additions, followed by a small number of shifts at the final stage. Comparative study shows that NEDA outperforms widely-used approaches such as MAC and DA in many aspects. Being a high speed architecture free of ROM, multiplication and subtraction, NEDA can also expose the redundancy existing in the adder array consisting of entries of 0 and 1. A hardware compression scheme is introduced to generate a butterfly structure with minimum number of additions. NEDA-based architecture for one dimensional DCT core is presented as an example.
Parallel high-speed architecture for EBCOT in JPEG2000

In this paper, a parallel high-speed architecture for EBCOT is proposed, based on the parallel mode in JPEG2000. It discovers the parallelism among three passes in bit-plane coding: two passes can work on the coding operation without the addition of coding processing elements (PE) by using parallel context modeling. So it manages to make two bits encoded in one clock cycle. In order to keep high throughput and reduce the memory requirement, the pipelined pass-switching arithmetic encoder is adopted. The experimental results show that the proposed architecture reduces the processing time by more than 16.6% compared with the pass-parallel mode architecture in Chiang et al. (2002) and by more than 38% compared with the serial mode architecture in Chen et al. (2001).
On gate leakage reduction in dynamic CMOS circuits

Scaling CMOS technology to next generation improves performance, increases transistor density, and reduces power dissipation per device. However, scaling also increases subthreshold and gate leakage currents, which greatly degrades circuit noise immunity and increases the chip total power dissipation. In this paper we propose a new leakage reduction circuit technique for domino dynamic CMOS circuits with no performance degradation and with minimal area overhead. Simulations show an 88% reduction in standby gate leakage using Berkeley predictive technology models (BPTM) of 65nm with no performance degradation over standard domino circuits. Simulations also show that the proposed technique is 17% more robust against loading variations compared to previously proposed leakage reduction techniques.