1,720,971 research outputs found
Enhancing HEVC Spatial Prediction by Context-based Learning
No full textDeep generative models have been recently employed to compress images, image residuals or to predict image regions. Based on the observation that state-of-the-art spatial prediction is highly optimized from a rate-distortion point of view, in this work we study how learning-based approaches might be used to further enhance this prediction. To this end, we propose an encoder-decoder convolutional network able to reduce the energy of the residuals of HEVC intra prediction, by leveraging the available context of previously decoded neigh-boring blocks. The proposed context-based prediction enhancement (CBPE) scheme enables to reduce the mean square error of HEVC prediction by 25% on average, without any additional signalling cost in the bitstream
Quality assessment of deep-learning-based image compression
No full textImage compression standards rely on predictive coding, transform coding, quantization and entropy coding, in order to achieve high compression performance. Very recently, deep generative models have been used to optimize or replace some of these operations, with very promising results. However, so far no systematic and independent study of the coding performance of these algorithms has been carried out. In this paper, for the first time, we conduct a subjective evaluation of two recent deep-learning-based image compression algorithms, comparing them to JPEG 2000 and to the recent BPG image codec based on HEVC Intra. We found that compression approaches based on deep auto-encoders can achieve coding performance higher than JPEG 2000, and sometimes as good as BPG. We also show experimentally that the PSNR metric is to be avoided when evaluating the visual quality of deep-learning-based methods, as their artifacts have different characteristics from those of DCT or wavelet-based codecs. In particular, images compressed at low bitrate appear more natural than JPEG 2000 coded pictures, according to a no-reference naturalness measure. Our study indicates that deep generative models are likely to bring huge innovation into the video coding arena in the coming years
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Depth Video Coding Technologies
No full textThe emergence of new three-dimensional (3D) multimedia services, such as 3D television and free viewpoint television, created a need for multiple-view multiplexing on 3D displays for more fluidity and scalability. However, coding and transmitting more views is costly. Depth-based formats allow synthesizing the required number of views at a reduced cost compared with video-only formats. Nevertheless, depth videos, alongside texture or color videos, should also be efficiently compressed. A number of depth video coding technologies have been proposed in recent years, both in academic and industrial contexts. These technologies can be mainly categorized into three families. We have tools that exploit some inherent characteristics of depth maps, tools that exploit the correlations with the associated texture, and tools that optimize the depth map coding for the quality of the synthesis. This chapter is intended as a tutorial, illustrating those different depth coding tools, while giving corresponding examples of each. © 2013 by John Wiley & Sons, Ltd
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Subjective and Objective Quality Assessment of the SoftCast Video Transmission Scheme
No full textSoftCast-based linear video coding and transmission (LVCT) schemes have been proposed as a promising alternative to traditional video coding and transmission schemes in wireless environments. Currently, the performance of LVCT schemes is evaluated by means of traditional objective scores such as PSNR or SSIM. Nevertheless, since the compression is performed in a very different way from traditional coding schemes such as HEVC, visual artifacts are also quite different and deserve to be subjectively assessed. In this paper, we propose a subjective quality assessment of SoftCast, pioneer and standard of the LVCT schemes. This study aims to better understand the trade-offs between the LVCT parameters that can be tuned to improve the quality. These parameters, including different GoP-sizes, Compression Ratios (CR) and Channel Signal-to-Noise Ratio (CSNR), are used to generate a dataset of 85 videos. A Double Stimulus Impairment Scale (DSIS) test is performed on the received videos to assess the perceived quality. Results show that the key characteristic of SoftCast, the linear relation between CSNR and PSNR, is also observed with the Mean-Opinion Scores (MOS), except at high CSNR where the quality saturates. In addition, Bjontegaard model is used to quantify the trade-offs between CR, GoP-size and CSNR, depending on the intended application. Finally, the performance of objective metrics compared to the obtained MOS is evaluated. Results show that Multi-Scale SSIM (MS-SSIM), SSIM and Video Multimethod Assessment Fusion (VMAF) metrics offer the best correlation with the MOS values
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