PEA265: Perceptual Assessment of Video Compression Artifacts

Abstract

The most widely used video encoders share a common hybrid coding framework that includes block-based motion estimation/compensation and block-based transform coding. Despite their high coding efficiency, the encoded videos often exhibit visually annoying artifacts, denoted as Perceivable Encoding Artifacts (PEAs), which significantly degrade the visual Qualityof-Experience (QoE) of end users. To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs. In this work, we make the first attempt to build a large-scale subjectlabelled database composed of H.265/HEVC compressed videos containing various PEAs. The database, namely the PEA265 database, includes 4 types of spatial PEAs (i.e. blurring, blocking, ringing and color bleeding) and 2 types of temporal PEAs (i.e. flickering and floating). Each containing at least 60,000 image or video patches with positive and negative labels. To objectively identify these PEAs, we train Convolutional Neural Networks (CNNs) using the PEA265 database. It appears that state-of-theart ResNeXt is capable of identifying each type of PEAs with high accuracy. Furthermore, we define PEA pattern and PEA intensity measures to quantify PEA levels of compressed video sequence. We believe that the PEA265 database and our findings will benefit the future development of video quality assessment methods and perceptually motivated video encoders.

Automated River Plastic Monitoring Using Deep Learning and Cameras

“…While RGB video cameras are readily available worldwide, there are at least four limitations in terms of their imaging quality. The first limitation is the H.265 video compression employed that induces perceivable encoding artifacts (Lin et al., 2019) that may impede image quality and hence counting precision.…”

“…At each location, the camera recorded continuous sequences of a 1080p video stream with H.265 compression for 3 to 10 days. Despite the high compression quality, H.265 still exhibits perceivable encoding artifacts (Lin et al., 2019), which may negatively affect our detection results. The same camera was used for all experiments, the Dahua Easy4ip IPC‐HDBW1435EP‐W.…”

An efficient post-processing adaptive filtering technique to rectifying the flickering effects

“…Due to a one-dimensional filtering approach, the in-loop processing method enhances the coding efficiency by reducing blocking artifacts amongst adjoining pixels or frames but is unable to process corner outliers. To alleviate blocking artifacts different post-processing approaches such as frequency domain analysis , Projection Onto Convex Sets (POCS) [9][10][11][12][13], waveletbased techniques [8,[20][21][22][23][24][25][26][27][28][29][30], estimation theory [5,[9][10][11][12][13], and filtering approach [11][12][13][14][15] has been proposed in last few decades. The most common method is to apply a low-pass filter across block boundaries to remove artifacts.…”

“…On the other hand, corner outliers, detection, and removal have been proposed by [15,25]. During compression, the corner outlier pixels are either considerable value or very small value pixels concerning surrounding pixels [8,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Later on, Wang J. et al [37] presented an adaptive filter-based technique for compressed images of different regions.…”

Multi-Dimensional Feature Fusion Network for No-Reference Quality Assessment of In-the-Wild Videos

“…The authors of [ 49 ] provided an efficient deep-learning metric called DIQM to reduce the computational complexity in mimicking the HVS. For perceivable encoding artifacts (PEAs), [ 50 ] proposed a CNN network for identifying different kinds of distortions. For convolutional neural networks and multi-regression-based evaluation (COME), [ 51 ] proposed a multi-regression model to imitate human psychological perception.…”