16,405 research outputs found
PEA265: Perceptual Assessment of Video Compression Artifacts
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.Comment: 10 pages,15 figures,4 table
Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric
Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively
Simple and fast subband de-blocking technique by discarding the high band signals
In this paper, we propose a simple and fast post-processing de-blocking technique to reduce blocking artifacts. The block-based coded image is first decomposed into several subbands. Only the low frequency subband signals are retained and the high frequency subband signals are discarded. The remaining subband signals are then reconstructed to obtain a less blocky image. The ideas are demonstrated by a cosine filter bank and a modulated sine filter bank. The simulation result shows that the proposed algorithm is effective in the reduction of blocking artifacts
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
Lossy image compression algorithms are pervasively used to reduce the size of
images transmitted over the web and recorded on data storage media. However, we
pay for their high compression rate with visual artifacts degrading the user
experience. Deep convolutional neural networks have become a widespread tool to
address high-level computer vision tasks very successfully. Recently, they have
found their way into the areas of low-level computer vision and image
processing to solve regression problems mostly with relatively shallow
networks.
We present a novel 12-layer deep convolutional network for image compression
artifact suppression with hierarchical skip connections and a multi-scale loss
function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an
improvement of up to 0.36 dB over the best previous ConvNet result. We show
that a network trained for a specific quality factor (QF) is resilient to the
QF used to compress the input image - a single network trained for QF 60
provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.Comment: 8 page
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