Benny Lövström

No-reference image and video quality assessment: a classification and review of recent approaches

The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods.

A reduced complexity no-reference artificial neural network based video quality predictor

There is a growing need for robust methods for reference free perceptual quality measurements due to the increasing use of video in hand-held multimedia devices. These methods are supposed to consider pertinent artifacts introduced by the compression algorithm selected for source coding. This paper proposes a model that uses readily available encoder parameters as input to an artificial neural network to predict objective quality metrics for compressed video without using any reference and without need for decoding. The results verify its robustness for prediction of objective quality metrics in general and for PEVQ and PSNR in particular. The paper also focuses on reducing the complexity of the neural network.

Subjective quality assessment of H.264/AVC encoded low resolution videos

Advancements in the video processing area have been proliferated by services that require low delay. Such services involve applications being offered at various temporal and spatial resolutions. It necessitates to study the impacts of related video coding conditions upon perceptual quality. But most of studies concerned with quality assessment of videos affected by coding distortions lack in variety of spatio-temporal resolutions. This paper presents a work done on quality assessment of videos encoded by state-of-the-art H.264/AVC standard at different bitrates and frame rates. Overall, 120 test scenarios for video sequences having different spatial and temporal spectral information were studied. The used coded bistreams in this work and the corresponding subjective assessment scores have been made public for the research community to facilitate further studies.

A no-reference machine learning based video quality predictor

The growing need of quick and online estimation of video quality necessitates the study of new frontiers in the area of no-reference visual quality assessment. Bitstream-layer model based video quality predictors use certain visual quality relevant features from the encoded video bitstream to estimate the quality. Contemporary techniques vary in the number and nature of features employed and the use of prediction model. This paper proposes a prediction model with a concise set of bitstream based features and a machine learning based quality predictor. Several full reference quality metrics are predicted using the proposed model with reasonably good levels of accuracy, monotonicity and consistency.

Predicting full-reference video quality measures using HEVC bitstream-based no-reference features

This paper presents bitstream-based features for perceptual quality estimation of HEVC coded videos. Various factors including the impact of different sizes of block-partitions, use of reference-frames, the relative amount of various prediction modes, statistics of motion vectors and quantization parameters are taken into consideration for producing 52 features relevant for perceptual quality prediction. The used test stimuli constitutes 560 bitstreams that have been carefully extracted for this analysis from the 59, 520 bistreams of the large-scale database generated by the Joint Effort Group (JEG) of the Video Quality Experts Group (VQEG). The obtained results show the significance of the considered features through reasonably accurate and monotonic prediction of a number of objective quality metrics.

Modulation Domain Adaptive Gain Equalizer for Speech Enhancement

This paper evaluates speech enhancement by filtering in the modulation frequency domain, as an alternative to filtering in conventional frequency domain. Adaptive Gain Equalizer (AGE) is a commonly ...

A No Reference Video Quality Metric Based on Jerkiness Estimation Focusing on Multiple Frame Freezing in Video Streaming

ABSTRACT In wireless networks, due to limited bandwidth and packet losses, seamless and ubiquitous delivery of high-quality video streaming services is a big challenge for the operators. In order to improve the process of online video quality monitoring, the presence of no reference (NR) objective video quality assessment (VQA) methods is required. In some networks, the video decoder on the reception side adopts a mechanism in which last correctly received frame is frozen and displayed on video display terminal until the next correct frame is received. This phenomenon, employed as an error concealment technique, can cause a perceptual jerkiness on the video display terminal. In this paper, we have proposed an enhanced model of objective VQA based on the estimation of jerkiness. A study of three contemporary NR methods, used for objective VQA and online monitoring of videos, has been included along with subjective VQA tests. The subjective tests were performed for a set of video sequences with specific spatial and temporal information. The proposed NR method is based on our careful observations from the subjective test results and our main focus is to cater the effect of multiple frame freeze impairments in video steaming. Comparison with other NR methods shows that the proposed method performs better, in terms of estimating the impact of multiple frame freezing impairments, and has more affinity with the subjective test results.

Effect of content characteristics on quality of experience of adaptive streaming

The growing popularity of adaptive streaming-based video delivery nowadays has raised the interest about the users perception when experiencing quality adaptation. The impact of the video content characteristics on users perceptual quality has already become evident. The aim of this study is to investigate the influence of this factor on the quality of experience of adaptive streaming scenarios. Our results show that the perceptual quality of adaptation strategies applied on videos with high spatial and low temporal amount of activity is significantly lower compared to the other content types.

A no-reference bitstream-based perceptual model for video quality estimation of videos affected by coding artifacts and packet losses

In this work, we propose a No-Reference (NR) bitstream-based model for predicting the quality of H.264/AVC video sequences, affected by both compression artifacts and transmission impairments. The proposed model is based on a feature extraction procedure, where a large number of features are calculated from the packet-loss impaired bitstream. Many of the features are firstly proposed in this work, and the specific set of the features as a whole is applied for the first time for making NR video quality predictions. All feature observations are taken as input to the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. LASSO indicates the most important features, and using only them, it is possible to estimate the Mean Opinion Score (MOS) with high accuracy. Indicatively, we point out that only 13 features are able to produce a Pearson Correlation Coefficient of 0.92 with the MOS. Interestingly, the performance statistics we computed in order to assess our method for predicting the Structural Similarity Index and the Video Quality Metric are equally good. Thus, the obtained experimental results verified the suitability of the features selected by LASSO as well as the ability of LASSO in making accurate predictions through sparse modeling.

Subband modulator Kalman filtering for single channel speech enhancement

This paper presents a single channel speech enhancement technique based on sub-band modulator Kalman filtering for laryngeal (normal) and a laryngeal (Esophageal speech) speech signals. The noisy speech signal is decomposed into sub-bands and subsequently each sub-band is demodulated into its modulator and carrier components. Kalman filter is applied to modulators of all sub-bands without altering the carriers. Performance of the proposed system has been validated by Mean Opinion Score (MOS) for laryngeal and Harmonic to Noise Ratio (HNR) for a laryngeal speech. An improvement of 20% has been observed in MOS over sub-band Kalman filtering for laryngeal speech, while 3 to 4 dB enhancement in HNR has been observed for a laryngeal speech over the full-band Kalman filtering.