We previously proposed a machine learning based post filtering method for reducing image artifacts caused by lossy compression. The method classifies reconstructed image samples into three categories using a support vector machine (SVM) to roughly discriminate magnitude of the reconstruction errors. Then, an optimum offset value is added to the samples belonging to each category in a similar way to the post filtering technique called sample adaptive offset (SAO) used in the H.265/HEVC standard. In this paper, two kinds of SVM classifiers are adaptively switched according to information on block boundaries of transform units (TUs) in H.265/HEVC intra-frame coding. Furthermore, samples used for a feature vector, which will be fed to the SVM classifier, are rotated at the block boundary to properly capture local characteristics of the reconstruction errors.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.