1,721,075 research outputs found
MLIC: Multi-Reference Entropy Model for Learned Image Compression
Get PDFRecently, learned image compression has achieved remarkable performance. The entropy model, which estimates the distribution of the latent representation, plays a crucial role in boosting rate-distortion performance. However, most entropy models only capture correlations in one dimension, while the latent representation contain channel-wise, local spatial, and global spatial correlations. To tackle this issue, we propose the Multi-Reference Entropy Model (MEM) and the advanced version, MEM. These models capture the different types of correlations present in latent representation. Specifically, We first divide the latent representation into slices. When decoding the current slice, we use previously decoded slices as context and employ the attention map of the previously decoded slice to predict global correlations in the current slice. To capture local contexts, we introduce two enhanced checkerboard context capturing techniques that avoids performance degradation. Based on MEM and MEM, we propose image compression models MLIC and MLIC. Extensive experimental evaluations demonstrate that our MLIC and MLIC models achieve state-of-the-art performance, reducing BD-rate by and on the Kodak dataset compared to VTM-17.0 when measured in PSNR. Our code is available at https://github.com/JiangWeibeta/MLIC.Accepted to ACMMM 202
Multiple LAN Internet Protocol Converter (MLIC) for Multimedia Conferencing
No full textMultimedia conferencing over multiple Local Area Networks (LAN), or across a Wide Area Network requires the use of efficient multicast servers in order to function correctly. This paper presents a proposed Multiple LAN Internet Protocol Converter (MLIC) algorithm currently being implemented by the Network Research Group and Multimedia Research Labs Sdn. Bhd. for use with multimedia conferencing, to interconnect multiple wide area LANs using IP protocols. This proposed multimedia multicast router will allow multicast based multimedia traffic over ATM Networks like TEMAN (Testbed Environment for Malaysia Multimedia Applications and Networking) and APAN (Asia Pacific Advanced Networks). Existing wide-area multimedia conferencing architectures are designed using proprietary systems and routers for wide area multicasting. With MLIC, a distributed approach using off the shelf hardware is proposed. MLICs maintains the full duplex interaction between active multimedia conferencing clients via ..
MLIC-Synthetizer: a Synthetic Multi-Light Image Collection Generator
Get PDFWe present MLIC-Synthetizer, a Blender plugin specifically designed for the generation of a syntethic Multi-Light Image Collection using physically-based rendering. This tool makes easy to generate large amount of test data that can be useful for Photometric Stereo algorithms evaluation, validation of Reflectance Transformation Imaging calibration and processing method, relighting methods and more. Multi-pass rendering allows the generation of images with associated shadows and specularity ground truth maps, ground truth normals and material segmentation masks. Furthermore loops on material parameters allows the automatic generation of datasets with pre-defined material parameters ranges that can be used to train robust learning-based algorithms for 3D reconstruction, relight and material segmentation
MLIC++: Linear Complexity Multi-Reference Entropy Modeling for Learned Image Compression
Get PDFRecently, learned image compression has achieved impressive performance. The
entropy model, which estimates the distribution of the latent representation,
plays a crucial role in enhancing rate-distortion performance. However,
existing global context modules rely on computationally intensive quadratic
complexity computations to capture global correlations. This quadratic
complexity imposes limitations on the potential of high-resolution image
coding. Moreover, effectively capturing local, global, and channel-wise
contexts with acceptable even linear complexity within a single entropy model
remains a challenge. To address these limitations, we propose the Linear
Complexity Multi-Reference Entropy Model (MEM++). MEM++ effectively captures
the diverse range of correlations inherent in the latent representation.
Specifically, the latent representation is first divided into multiple slices.
When compressing a particular slice, the previously compressed slices serve as
its channel-wise contexts. To capture local contexts without sacrificing
performance, we introduce a novel checkerboard attention module. Additionally,
to capture global contexts, we propose the linear complexity attention-based
global correlations capturing by leveraging the decomposition of the softmax
operation. The attention map of the previously decoded slice is implicitly
computed and employed to predict global correlations in the current slice.
Based on MEM++, we propose image compression model MLIC++. Extensive
experimental evaluations demonstrate that our MLIC++ achieves state-of-the-art
performance, reducing BD-rate by 13.39% on the Kodak dataset compared to
VTM-17.0 in PSNR. Furthermore, MLIC++ exhibits linear GPU memory consumption
with resolution, making it highly suitable for high-resolution image coding.
Code and pre-trained models are available at
https://github.com/JiangWeibeta/MLIC.Comment: Compared with version presented at Neural Compression Workshop, ICML
2023 at OpenReview, in this arxiv version, we add the details of our prior
work presented at ACMMM 2023, new comparisons on complexity, and more
ablation studie
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
BASS-MLIC: a Novel Synthetic Dataset for Single-View Inverse Rendering Tasks on Cultural Heritage Artifacts
No full textBASS-MLIC is a synthetic dataset created with Blender to support multi-light image processing and inverse rendering tasks. It features orthographic views of culturally significant surfaces rendered with realistic materials and includes rich ground truth annotations, such as normals, depth, shadows, materials, and BRDF parameters. These annotations enable evaluation across diverse tasks like relighting, Photometric Stereo, shadow-aware estimations, and BRDF fitting. Preliminary experiments highlight its practical utility.Smart Tools and Applications in Graphics - Eurographics Italian Chapter ConferencePoster
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
MLIC-Synthetizer: a Synthetic Multi-Light Image Collection Generator
No full textWe present MLIC-Synthetizer, a Blender plugin specifically designed for the generation of a syntethic Multi-Light Image Collection using physically-based rendering. This tool makes easy to generate large amount of test data that can be useful for Photometric Stereo algorithms evaluation, validation of Reflectance Transformation Imaging calibration and processing method, relighting methods and more. Multi-pass rendering allows the generation of images with associated shadows and specularity ground truth maps, ground truth normals and material segmentation masks. Furthermore loops on material parameters allows the automatic generation of datasets with pre-defined material parameters ranges that can be used to train robust learning-based algorithms for 3D reconstruction, relight and material segmentation.Smart Tools and Apps for Graphics - Eurographics Italian Chapter ConferencePoster
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