354 research outputs found
Perceptually-Driven Video Coding with the Daala Video Codec
The Daala project is a royalty-free video codec that attempts to compete with
the best patent-encumbered codecs. Part of our strategy is to replace core
tools of traditional video codecs with alternative approaches, many of them
designed to take perceptual aspects into account, rather than optimizing for
simple metrics like PSNR. This paper documents some of our experiences with
these tools, which ones worked and which did not. We evaluate which tools are
easy to integrate into a more traditional codec design, and show results in the
context of the codec being developed by the Alliance for Open Media.Comment: 19 pages, Proceedings of SPIE Workshop on Applications of Digital
Image Processing (ADIP), 201
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
Removal Of Blocking Artifacts From JPEG-Compressed Images Using An Adaptive Filtering Algorithm
The aim of this research was to develop an algorithm that will produce a considerable improvement in the quality of JPEG images, by removing blocking and ringing artifacts, irrespective of the level of compression present in the image. We review multiple published related works, and finally present a computationally efficient algorithm for reducing the blocky and Gibbs oscillation artifacts commonly present in JPEG compressed images. The algorithm alpha-blends a smoothed version of the image with the original image; however, the blending is controlled by a limit factor that considers the amount of compression present and any local edge information derived from the application of a Prewitt filter. In addition, the actual value of the blending coefficient (α) is derived from the local Mean Structural Similarity Index Measure (MSSIM) which is also adjusted by a factor that also considers the amount of compression present. We also present our results as well as the results for a variety of other papers whose authors used other post compression filtering methods
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