CPIPQ: A common platform for silicon IP qualification

The main goal of hiding data is to conceal the very existence of the hidden information, therefore there is a significant demand for steganographic approaches that can ensure imperceptibility of such infromation. However, there is a limited corresponding evaluation parameters available. Most of the studies use the Peak Signal to Noise Ratio (PSNR) as a metric for imperceptibility evaluation, although it could provide less accurate results than the Human Visual System (HVS) evaluation. This paper provides a review of the existent evaluation metrics that are used to assess the quality of steganography. The examination of the correlation between the existing objective and subjective metrics is also conducted. Pixel differences metrics have a poor correlation with the subjective metrics, hence the HSV based metrics have better correlation than pixel metrics.

To deal with congestion control in internet protocol (IP) networks, different active queue management schemes have been successfully proposed. However, many of these schemes do not take into consideration the particular Voice over Internet Protocol (VoIP) requirements, thereby potentially penalizing this traffic. In this paper, we propose and evaluate a dropping packet selection algorithm for queue management schemes. This algorithm, called Drop-SwD , protects VoIP traffic without affecting other User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) traffic. Additionally, to preserve the quality of the VoIP service, the proposed algorithm selects the lowest quality packets (with less impact on the subjective quality) to be dropped. The application of the algorithm in different active queue management schemes is evaluated using objective and subjective quality metrics and intelligibility metrics as well. As a result, we show that Drop-SwD improves the final quality of the voice packets’ flow, without degrading the performance of other traffic class, with respect to the packet dropping schemes of reference.

We evaluate the performance of three state of the art video codecs on synthetic videos. The evaluation is based on both subjective and objective quality metrics. The subjective quality of the compressed video sequences is evaluated using the Double Stimulus Impairment Scale (DSIS) assessment metric while the Peak Signal-to-Noise Ratio (PSNR) is used for the objective evaluation. An extensive number of experiments are conducted to study the effect of frame rate and resolution on codecs’ performance for synthetic videos. The evaluation results show that video codecs respond in different ways to frame rate and frame resolution change. H.264 shows superior capabilities compared to other codecs. Mean Opinion Score (MOS) results are shown for various bitrates, frame rates and frame resolutions.

Scalable video coding is emerging as an efficient alternative to simulcast encoding to distribute the same video content simultaneously to many users having different terminals and network conditions. In order to select the best combination of video scalability options for a given network condition and content, the availability of objective metrics that can reliably predict the video quality of scalable video sequence is crucial. In this paper, we propose a performance evaluation study of a set of state of the art Full-Reference and No-Reference metrics, considering a public database of test video sequences and related subjective quality annotations. The results demonstrate that most of the considered metrics show lack of robustness as predictors of subjective quality when both spatial and temporal quality distortions occur.

Omnidirectional image and video have gained popularity thanks to availability of capture and display devices for this type of content. Recent studies have assessed performance of objective metrics in predicting visual quality of omnidirectional content. These metrics, however, have not been rigorously validated by comparing their prediction results with ground-truth subjective scores. In this paper, we present a set of 360-degree images along with their subjective quality ratings. The set is composed of four contents represented in two geometric projections and compressed with three different codecs at four different bitrates. A range of objective quality metrics for each stimulus is then computed and compared to subjective scores. Statistical analysis is performed in order to assess performance of each objective quality metric in predicting subjective visual quality as perceived by human observers. Results show the estimated performance of the state-of-the-art objective metrics for omnidirectional visual content. Objective metrics specifically designed for 360-degree content do not outperform conventional methods designed for 2D images.

Most recently, due to the demand of immersive communication, region-of-interest-based (ROI) high efficiency video coding (HEVC) approaches in conferencing scenarios have become increasingly important. However, there exists no objective metric, specially developed for efficiently evaluating the perceived visual quality of video conferencing coding. Therefore, this paper proposes a novel objective quality assessment method, namely Gaussian mixture model based peak signal-to-noise ratio (GMM-PSNR), for the perceptual video conferencing coding. First, eye tracking experiments, together with a real-time technique of face and facial feature extraction, are introduced. In the experiments, importance of background, face, and facial feature regions is identified, and it is then quantified based on eye fixation points over test videos. Next, assuming that the distribution of the eye fixation points obeys Gaussian mixture model, we utilize expectation-maximization (EM) algorithm to generate an importance weight map for each frame of video conferencing coding, in light of a new term eye fixation points/pixel (efp/p). According to the generated weight map, GMM-PSNR is developed for quality assessment by assigning different weights to the distortion of each pixel in the video frame. Finally, we utilize some experiments to investigate the correlation of the proposed GMM-PSNR and other conventional objective metrics with subjective quality metrics. The experimental results show the effectiveness of GMM-PSNR.

This paper presents an automatic method for simultaneous detection and elimination of impulsive noises from audio signals. The method is based on the sparse signal decomposition (SSD) with overcomplete hybrid dictionary (OHD) matrix including impulse and sinusoidal waveforms derived from discrete impulse, sine and cosine functions, and 1-norm optimization algorithm. The SSD technique decomposes an audio signal into two parts: an approximation part including the sinusoidal components; and a detail part including the impulsive and background noise components. Results show that the impulse waveforms can adequately capture clicks, pops and record scratches in audio signals meanwhile the sinusoids of the OHD matrix can effectively capture sinusoidal components. For performance comparison purpose, four restoration methods are implemented based on the representation dictionaries, median filter, and signal dependent rank order mean techniques. The methods are validated using audio signals extracted from old archive Indian and English movies and standard music database. The quality of the restored audio signals is evaluated using both subjective quality and objective quality metrics. Results show that the proposed method effectively removes impulsive noises without degrading the perceived audio quality and its intelligibility as compared to the existing restoration methods.

Interest in playing/streaming multimedia content in Full High Definition (Full HD) and the upcoming Ultra High Definition (UHD) is rapidly increasing. However, TVs with such resolutions has higher hardware and bandwidth requirements. Hence, advanced compression tools are essential to provide TV services in Full HD and UHD quality. The aim of this paper is to explore coding efficiency performance of High Efficiency Video Coding (HEVC) and VP9 compression tools for video content in Full HD and UHD resolutions. Performance of both video compression tools is evaluated and compared with objective and subjective video quality metrics. The results show that the HEVC and VP9 codecs for Full HD and UHD videos have comparable coding efficiency. Moreover, in several cases, Quality of Experience (QoE) for Full HD, depending on the video content and target bitrate, can outperform QoE for UHD.

2.02 - Physical Parameters of Image Quality

Today’s scenario of semiconductor technology is a tremendous innovation; it includes a large number of intellectual property (IP) cores, interconnects, or buses in system on chip (SOC) design and based upon the necessity its complexity keeps on increasing. Hence, for the communication between these IP cores, a standard protocol is developed. The necessity of IP reuse, abridging the design time and the complexity makes large-scale SOC more challenging in order to endorse IP core reusability for SOC designs. An efficient non-proprietary protocol for communication between IP cores is open core protocol (OCP). OCP comes under socket-based interface and openly licensed core concentric protocol. This paper addresses on the verification of implemented design of OCP. The proposed paper is to verify the implemented design by using System Verilog and Universal Verification Methodology (UVM) in SimVision tool.

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As in any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive benchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were benchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a total of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were computed to assess the accuracy, monotonicity, and consistency of the metric estimation of subjective scores. Statistical analysis was performed on the performance indexes to discriminate small differences between metrics. Results demonstrated that metrics designed for HDR content, i.e., HDR-VDP-2 and HDR-VQM, are the most reliable predictors of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can be improved by considering non-linearities of the human visual system, while further efforts are necessary to improve performance of no-reference quality metrics for HDR content.

Modern Systems-on-a-Chip (SoC) are constructed by composition of IP (Intellectual Property) Cores with the communication between these IP Cores being governed by well described interaction protocols. However, there is a disconnect between the machine readable specification of these protocols and the verification of their implementation in known hardware description languages. Although tools can be written to address such a separation of concerns, such tooling is often hand written and used to check hardware designs a posteriori. We have developed a dependent type-system and proof-of-concept modelling language to reason about the physical structure of hardware interfaces respective to user provided descriptions. Our type-system provides correct-by-construction guarantees that the interfaces on an IP Core will be well-typed if they adhere to a specified standard.

This paper introduces a multi-channels data synchronous acquisition and processing IP (intellectual property) core for digital protective relay. It integrates data acquisition and processing in one FPGA, resolving problem of asynchrony between data acquisition and data processing as well as setting of the number of sampling channels and sampling points per period. In contrast to the traditional acquisition system, it has characteristic of high speed, flexibility and low cost. This IP core was verified in FPGA (field programmable gate array) based hardware platform. The result of experiment indicates that this IP core is correct and can be used into SOC (system on chip) application as a single module.

There is a significant increase in mobile internet usage resulting in demand for traffic volume, frequency efficiency and energy and cost reduction, for which small cell networks are expected to play a key role. Due to high bandwidth requirement, mobile health applications (m-health) and medical video streaming in particular will benefit from small cell networks in terms of Quality of Service (QoS) and Quality of Experience (QoE) enhancements. This paper presents content-aware and device-aware medical Quality of Experience evaluations in terms of subjective (e.g. MOS) and objective (e.g. PSNR and SSIM) quality metrics obtained over small cell networks. Furthermore, we address the following two main research questions: (1) How significant is ultrasound video content type in determining medical QoE? (2) How much of a role does the display device play in medical experts’ diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatio-temporal features and validating their significance through medical experts’ subjective ratings. The latter is answered by conducting a subjective experiment of the ultrasound video sequences across multiple devices, ranging in screen size and resolution.