1,720,954 research outputs found
Research on Self-supervised Learning for Image/Video Compression
Get PDFIntegrating deep learning techniques into the video coding framework gains significant improvement compared to the standard compression techniques, especially, self-supervised learning approaches such as Super-Resolution, Colorization. In this work, we reduce the bit-rate significantly by removing pixels (down-sampling) and color information. It creates a new challenge in compensating for the massively missing information through down-sampling, color removal, and compression. Adopting advanced techniques in computer vision, we propose a specific deep neural network, named restoration-reconstruction deep neural network (RR-DnCNN), to solve Super-Resolution with compression degradation. Furthermore, we also introduce a scheme to compensate for color information with Color Learning and enhance image quality with Deep Motion Compensation for P-frame coding. As a result, our works outperform the standard codec such as H.265/HEVC and the previous works in the field. Furthermore, our work achieves the world\u27s top 5 performance on the P-frame track of Challenge on Learned Image Compression (CLIC), Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020
A Research on Learned Image/Video Restoration and Compression for Solving Real-World Degradation
Get PDFThe adage, "A picture is worth a thousand words", has proved the effectiveness of image and video in delivering information. Hence, the Internet becomes wonderful when we can share image/video media with people worldwide in this digital era. It must be more incredible if image/video media can precisely show what we see in real life with our eyes. Unfortunately, due to natural causes (e.g., shooting devices and environments) or artificial causes (e.g., image/video compression sacrificing information to achieve better transmission), the image/video media is not always in the best visual quality which human expects to see (ground-truth), reducing user experience in receiving the information. The loss of an image compared with its ground-truth is called degradation, and the act of solving degradation is called restoration. Even though many advanced techniques have been proposed to restore degraded images/videos, the real-world degradation remains unsolved. Hence, this thesis will dive into and solve specific types of real-world degradation, including (1) artificial degradation in image/video compression and (2) naturally affected degradation in smartphone photo scanning.Regarding (1), we leverage deep learning techniques to solve compression degradation and recover other missing information caused by our effort in reducing compression complexity. Concretely, we sacrifice numerous pixels by down-sampling and color information. It creates a new challenge in compensating for the massively missing information through down-sampling, color removal, and compression. By adopting advanced techniques in computer vision, we propose a specific deep neural network, named restoration-reconstruction deep neural network (RR-DnCNN), to solve Super-Resolution with compression degradation. Furthermore, we also introduce a scheme to compensate for color information with Color Learning and enhance image quality with Deep Motion Compensation for P-frame coding. As a result, our works outperform the standard codec and the previous works in the field.Regarding (2), one solution is to train a supervised deep neural network on many digital images and smartphone-scanned versions. However, it requires a high labor cost, leading to limited training data. Previous works create training pairs by simulating degradation using low-level image processing techniques. Their synthetic images are then formed with perfectly scanned photos in latent space. Even so, the real-world degradation in smartphone photo scanning remains unsolved since it is more complicated due to lens defocus, low-cost cameras, losing details via printing. Besides, locally structural misalignment still occurs in data due to distorted shapes captured in a 3-D world, reducing restoration performance and the reliability of the quantitative evaluation. To address these problems, we propose a semi-supervised Deep Photo Scan (DPScan). First, we present a way to produce real-world degradation and provide the DIV2K-SCAN dataset for smartphone-scanned photo restoration. Also, Local Alignment is proposed to reduce the minor misalignment remaining in data. Second, we simulate many different variants of the real-world degradation using low-level image transformation to gain a generalization in smartphone-scanned image properties, then train a degradation network to learn how to degrade unscanned images as if a smartphone scanned them. Finally, we propose a Semi-Supervised Learning that allows our restoration network to be trained on both scanned and unscanned images, diversifying training image content. As a result, the proposed DPScan quantitatively and qualitatively outperforms its baseline architecture, state-of-the-art academic research, and industrial products in the field.博士(工学)法政大学 (Hosei University
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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