nils-herrmann/clean_scirepeval huggingface dataset:
Product code and recurrent alternative decoding for wireless image transmission

We have developed a novel channel coding scheme for image transmission over wireless channels. A key component of this scheme is the construction of the product code using a convolutional code and a recursive systematic code (RSC) in the horizontal and vertical directions, respectively. High performance transmission has been achieved through an innovative recurrent alternative decoding scheme based on constrained Viterbi decoding. We used RSC because of its systematic structure so that we did not need to transmit the original data portion of the RSC coding outcome since this portion can be obtained from the other direction of channel decoding. At the receiving end, the data will at first be decoded along the horizontal direction. After decoding, the position of a correctly decoded row will be assigned a positive sign and be preserved. When we decode along the vertical direction, the positive signs after horizontal decoding will be used as constraints in the Viterbi decoding algorithm to force the survival path's passing through these known positions of correctly decoded bits. The constrained Viterbi algorithm using known correct bits enables us to derive likely correct values for other bits. This reduces greatly possible paths along the trellis diagram even though some or the paths dropped may have smaller path cost due to channel noise corruption and would otherwise be preserved by the unconstrained Viterbi algorithm. Moreover, this also reduces significantly the comparisons between the branches and the received symbols. Similarly the use of the correctly localized columns are also preserved and used again to refine the next round of horizontal decoding. Such recurrent decoding will alternate between horizontal and vertical directions until all data is correctly decoded or until there is no more improvement in both directions. We investigated the performance of the proposed scheme for both BSC and GEC channels and report the results here.
Scalable Video Multicast for MU-MIMO Systems With Antenna Heterogeneity

In contemporary multiuser multiple-input multipleoutput systems, it is common for the reception devices to have a varying number of antennas. When multicast is performed, the number of concurrent spatial streams is limited by the device with the least number of antennas, which prevents more capable devices from getting higher rates. In this paper, we address the antenna heterogeneity in wireless video multicast by the innovative design of multiple similar description (MSD) video coding and multiplexed space-time block coding (M-STBC). MSD coding generates multiple descriptions of a video and features that any linear combinations of the descriptions are decodable. The descriptions comprising of real numbers are further processed by transform and power allocation steps for efficient transmission in a power-constrained system. M-STBC puts symbols in similar descriptions to corresponding space-time positions and ensures decodability under any antenna settings and channel conditions. As a result, we build up a scalable video multicast system, named AirScale, which allows receivers with a various number of antennas to decode from a single transmission, and the reconstructed video quality improves with the number of equipped antennas. Evaluations on Sora shows that, in a {1, 2, 3, 4} × 4 system, AirScale provides baseline quality for one-antenna receiver and a much higher quality for multiantenna receivers. The gain over SoftCast is up to 3.5, 3.9, and 4.1 dB for two-, three-, and four-antenna receivers, respectively.
Content-based multiple bitstream image transmission over noisy channels

In this paper, we propose a novel combined source and channel coding scheme for image transmission over noisy channels. The main feature of the proposed scheme is a systematic decomposition of image sources so that unequal error protection can be applied according to not only bit error sensitivity but also visual content importance. The wavelet transform is adopted to hierarchically decompose the image. The association between the wavelet coefficients and what they represent spatially in the original image is fully exploited so that wavelet blocks are classified based on their corresponding image content. The classification produces wavelet blocks in each class with similar content and statistics, therefore enables high performance source compression using the set partitioning in hierarchical trees (SPIHT) algorithm. To combat the channel noise, an unequal error protection strategy with rate-compatible punctured convolutional/cyclic redundancy check (RCPC/CRC) codes is implemented based on the bit contribution to both peak signal-to-noise ratio (PSNR) and visual quality. At the receiving end, a postprocessing method making use of the SPIHT decoding structure and the classification map is developed to restore the degradation due to the residual error after channel decoding. Experimental results show that the proposed scheme is indeed able to provide protection both for the bits that are more sensitive to errors and for the more important visual content under a noisy transmission environment. In particular, the reconstructed images illustrate consistently better visual quality than using the single-bitstream-based schemes.
A novel algorithm for computer-assisted measurement of cervical length from transvaginal ultrasound images

The cervical length measured by transvaginal ultrasound is a proven clinical tool for predicting premature birth. The standard manual measurement of the cervix is limited by variability in the technique. In this research, we develop the first computer algorithm that is able to identify the anatomic landmarks of the cervix on a transvaginal ultrasound image and determine the standard cervical length. The system is composed of four stages: The first stage is adaptive speckle suppression using variable length sticks algorithm. The second stage is the location of the internal cervical opening or "os" using a region-based segmentation. The third stage is delineation of the cervical canal. The fourth stage uses gray level summation patterns and prior knowledge to first localize the tissue boundary of the external cervix, and then use a template to determine the specific location of the external os. The cervical length is determined and calculated to image scale. To validate the proposed algorithm, 101 cervical ultrasound images were selected from a series of 37 examinations performed on 17 patients over an eight-month period. Repeated measurements of cervical length using the computer-assisted method were compared with those carried out by two experienced sonographers. The median intraobserver variability for the 101 images using the computer-assisted method was significantly smaller than that of the manual method by either sonographer. In a pairwise comparison, the mean cervical length for the computer method matches with the mean manual cervical length.
A-Lamp: Adaptive Layout-Aware Multi-patch Deep Convolutional Neural Network for Photo Aesthetic Assessment

Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, warping, or padding, which often alter image composition, reduce image resolution, or cause image distortion. Thus the aesthetics of the original images is impaired because of potential loss of fine grained details and holistic image layout. However, such fine grained details and holistic image layout is critical for evaluating an images aesthetics. In this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This novel scheme is able to accept arbitrary sized images, and learn from both fined grained details and holistic image layout simultaneously. To enable training on these hybrid inputs, we extend the method by developing a dedicated double-subnet neural network structure, i.e. a Multi-Patch subnet and a Layout-Aware subnet. We further construct an aggregation layer to effectively combine the hybrid features from these two subnets. Extensive experiments on the large-scale aesthetics assessment benchmark (AVA) demonstrate significant performance improvement over the state-of-the-art in photo aesthetic assessment.
Distributed multiview video coding using the fusion of triple side information

Traditional multiview video coding schemes exploit all types of correlations at the encoder. The inherent high encoder complexity and the needs for large volume data communication between cameras make them impractical for many applications. In this paper, we propose a novel distributed coding scheme for multiview video. All the views are separately encoded but jointly decoded. At the decoder, we design a fusion algorithm that is able to make full use of three types of side information: temporal prediction, interview motion prediction and inter-view luma prediction. This is the key innovation in this research. This fusion scheme is able to exploit both intra-view and inter-view correlations with a low complexity encoder. Experimental results indicate significant R-D performance gain over single view distributed video coding and traditional intra coding.
No-reference depth quality assessment for texture-plus-depth images

In 3D video (3DV) and free-viewpoint video (FVV), it is vitally important to detect the errors and assess depth quality. However, since ground-truth depth maps are often unattainable, assessing depth quality without reference becomes an imperative task for many applications. This research considers the texture-plus-depth format in 3DV and FVV, and focuses on the misalignment error at depth discontinuities. A matching algorithm between depth and texture edges is proposed to determine corresponding matching pairs and to identify serious mismatches. The matching procedure is based on both spatial distances and direction similarities between texture and depth edges, and the algorithm is performed between edge segments, instead of edge pixels, in order to improve the robustness of matching. Furthermore, an adaptive algorithm is designed to divide depth edges into segments with different lengths based on the curvature of the edges. After the matching is completed, misalignments between matching pairs are used to generate a no-reference assessment metric. Experimental results demonstrate that the proposed matching scheme is able to achieve accurate matches between depth and texture edges. More importantly, it has been shown that the correlations between the proposed metric and the widely accepted full-reference metric are greater than 0.9, making this no-reference depth quality assessment scheme suitable for contemporary 3DV and FVV applications.
A real-time monitoring system for the cardiac and respiratory parameters using bioimpedance technique

We have implemented a bioimpedance system based on a digital signal processor (DSP) chip (ADSP-2101, from Analog Device Corp.) and a personal computer (PC) with digital and modular design approaches for monitoring respiratory and cardiac parameters. The overall system contains a digital EPROM (eraseable programmable read-only-memory) oscillator, eight independent current generators, a voltmeter with digital demodulation, an amplifier, an I/O interface, and a friendly man-machine interface through the PC. Information such as respiratory flow, respiratory rate, tidal volume, heart rate, cardiac output, stroke volume and electrocardiograph (ECG) are presented in real time on the PC's monitor.
Robust image transmission with bidirectional synchronization and hierarchical error correction

We present a novel joint-source and channel image-coding scheme for noisy channel transmission. The proposed scheme consists of two innovative components: (1) intelligent bidirectional synchronization and, (2) layered bit-plane error protection. The bidirectional synchronization is able to recover the coding synchronization when any single or even when two consecutive synchronization codes are corrupted by the channel noise. With synchronized partition, unequal error protection for each bit plane can be designed based on the analysis of bit-plane error sensitivity for compressed image data transmission over noisy channels. Experimental results over extensive channel simulations show that the proposed scheme outperforms the well-known approach proposed by Sherwood and Zeger ).
From talking head to singing head: A significant enhancement for more natural human computer interaction

This paper proposes a 3D virtual animating head system, which can not only talk but also sing. With a reconstructed head mesh model, including external/internal articulators, from multi-source images, biology information are first used to visualize each phoneme with a musical note. The synchronicity between songs and articulatory movements is then modeled by a deep neural network trained on an audio/articulatory corpus. Finally, the visualization results of phonemes are blended by the synchronicity model to produce the song synchronized articulatory animations. Quantitative and qualitative improvements of singing ability on human computer interaction are demonstrated by comparing with other state-of-the-art talking head systems.
A comparison of the error resiliency of bit-plane based and symbol based pixel-domain distributed video coding

This work studies the error resilience of pixel-domain distributed video coding in noisy wireless transmission environment. Turbo codes are used to implement the DVC coder and the AWGN model is assumed for the transmission channel. The goal is to find out whether symbol based coding or bit-plane based coding is more robust against channel noise. We compare the two in the context of joint source-channel coding to ensure a high error resilience. First, we propose a framework to estimate the end-to-end distortion for these two schemes. Next, we allocate the rate between source coding and channel coding, aiming at a minimum end-to-end distortion. Then, we simulate the two schemes, setting the same bit budget for them, and compare their error resilience. Experimental results show that symbol based coding outperforms bit-plane based coding by up to 0.7 dB in PSNR of the decoded video.
Full Reference Quality Assessment for Image Retargeting Based on Natural Scene Statistics Modeling and Bi-Directional Saliency Similarity.

Image retargeting technology has been widely studied to adapt images for the devices with heterogeneous screen resolutions. Meanwhile effective objective retargeting quality assessment algorithms are also very important for optimizing and selecting favorable retargeting methods. Unlike previous assessment algorithms which rely on image local structure features and unidirectional prediction of information loss, we propose a bi-directional natural salient scene distortion model (BNSSD) including image natural scene statistics (NSS) measurement, salient global structure distortion measurement, and bi-directional salient information loss measurement. First, we propose a new NSS model in log-Gabor domain and verify its effectiveness in reflecting nature scene statistical distortions introduced during the retargeting process. Second, the concept of salient global structure distortion is proposed to measure the global structure uniformity in the corresponding salient regions between original and retargeted images. Finally, we propose a bidirectional salient information loss metric to measure the information loss between salient areas in original image and retargeted image. The effectiveness of the BNSSD model is verified on two widely recognized public databases, and the experimental results show that our method outperforms the state-of-the-art algorithms under different statistical assessment criteria.
Layered screen video coding leveraging hardware video codec

In this paper, we propose a layered screen video coding scheme based on existing video codecs to leverage hardware video codec for efficient screen video compression. In this scheme, the screen video compression is performed as two-layer coding: base layer coding and enhancement layer coding. The screen video is first analyzed in both frame and block levels for useful temporal and spatial information extraction to assist coding content selection in each layer. The non-skip screen frames are directly compressed by the conventional video codec in the base layer, while the screen contents sensitive to the video quality degradation are selected for improved coding in the enhancement layer. For contents to be enhanced, two intra coding modes are designed to improve the quality of the compressed text/graphics contents and suppress the artifacts introduced by chroma downsampling. The experimental results demonstrate that the screen video quality is improved objectively and subjectively by the proposed scheme with low cost on bitrate and computation complexity. Moreover, an average of 2.95dB coding gain is achieved in high bitrate.
Robust Video Transmission Over Packet Erasure Wireless Channels Based on Wyner-Ziv Coding of Motion Regions

This paper presents a new scheme for robust video transmission over packet erasure wireless channels based on Wyner-Ziv coding of motion regions. The multipath fading and shading of the wireless channels usually lead to loss or erroneous video packets which on occasions result in some spontaneous drop in video quality. Existing approaches with forward error correction (FEC) and error concealment have not been able to provide the desired robustness in video transmission. We develop a new scheme with a motion-based Wyner-Ziv coding (MWZC) by leveraging distributed source coding (DSC) ideas for error robustness. This new scheme is based on the fact that motion regions of a given video frame are particularly important in both objective and perceptual video quality and hence should be given preferential Wyner-Ziv coding based embedded protection. To achieve high coding efficiency, we determine the underlining motion regions based on a rate-distortion model. Within the framework of H.264/AVC specification, motion region determination can be efficiently implemented using flexible macroblock ordering (FMO) and data partitioning (DP). The bit stream generated by the proposed scheme consists two parts: the systematic portion generated from conventional H.264/AVC bit stream and the supplementary bit stream for error robust video transmission generated by the Wyner-Ziv coding of motion regions. Experimental results demonstrate that the proposed scheme significantly outperforms both decoder-based error concealment (DBEC) and conventional FEC with DBEC approaches.
Performance analysis for ZigBee under WiFi interference in smart home

Smart home not only makes people's lives more convenient, but also saves energy and daily expenses for households. Both WiFi and ZigBee are widely deployed in the smart home and operated in the same 2.4GHz ISM band, which results in the coexistence interference. Since the transmitting power of WiFi devices is much higher than that of ZigBee devices, ZigBee is more susceptible to coexisting interference. In this paper, we propose an analytical model to evaluate the performance of ZigBee under WiFi interference in the practical smart home scenario. By considering both channel access behavior and path loss behavior of the WiFi and ZigBee devices, the proposed model is built by involving both the Markov chain model and the indoor path loss model, and the ZigBee performance under WiFi interference is theoretically derived. The simulation results demonstrate the effectiveness of the proposed model for evaluating the ZigBee performance under WiFi interference.
Guest Editorial - Special section on cloud-based mobile media: Infrastructure, services, and applications

It is the aim of this special section to report on the latest research that explores various aspects of cloud-based mobile media. Through an open call for papers, we received 40 submissions. Fourteen (14) papers were accepted for final publication after two rounds of highly competitive reviews. The final papers were selected on the basis of originality and significance of the technical work, as well as relevance to the theme topic. The papers in this special section span a wide range of novel algorithms, techniques, applications, and system-level solutions. They naturally fall into two categories: addressing existing challenges or exploring emerging opportunities.
Scalable H.264/AVC Video Transmission Over MIMO Wireless Systems With Adaptive Channel Selection Based on Partial Channel Information

In this paper, we present a novel joint application physical-layer design (JAPLD) strategy to cost-effectively transmit scalable H.264/AVC video over multi-input multi-output (MIMO) wireless systems. With this approach, the application layer cooperates with the physical layer to maximize the system performance. First, in physical layer, we propose a new layered video transmission scheme over MIMO: adaptive channel selection (ACS). ACS-MIMO is fundamentally different from parallel transmission MIMO (PT-MIMO). While each bit stream is continuously transmitted through a fixed antenna in PT-MIMO, ACS-MIMO is able to periodically switch each bit stream among multiple antennas. In application layer, Scalable Video Coding (SVC) generates layered bit streams that need prioritized delivery. Then, we obtain the ordering of each subchannel's SNR strength as partial channel information (CI) at the receiver. The partial CI is acquired via the estimated channel state information based on training sequences. The JAPLD strategy we developed in this research shall switch the bit stream automatically to match the ordering of SNR strength for the subchannels. Essentially, we will launch higher priority layer bit stream into higher SNR strength subchannel by the proposed JAPLD algorithm. In this fashion, we can implicitly achieve automatic unequal error protection (UEP) for layered SVC transmission over MIMO system without power control at the transmitter. Experimental results show that the proposed ACS-MIMO system is able to achieve UEP with the obtained partial CI and the reconstructed video peak signal-to-noise ratio demonstrate the performance improvement of the proposed system as compared with open loop PT-MIMO system.
Bayesian based view synthesis for multi-planar structures

With the rapid development of mobile applications in recent years, there is a strong desire on light weight algorithm for view synthesis using uncalibrated images and limited geometry information. To address this challenge, we propose a Bayesian based view synthesis framework to support the rendering of complex scene with multiple planar structures. In this framework, we integrate image segmentation, reference plane selection and hole filling with a Bayesian formulation to perform view synthesis without using 3D geometry information. More specifically, we partition every reference image into multiple planes, estimate geometric and photometric parameters for each plane, synthesize the novel view by Bayesian modeling using selected reference planes, and refine the rendered image by a hole filling scheme. The entire view synthesis process is executed in an iterative manner to pursue high quality visual results. The experimental results show that the proposed method is able to achieve desired performance with less distortion and higher resolution.