Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling

Video Coding for Machines (VCM) aims to compress visual signals for machine analysis. However, existing methods only consider a few machines, neglecting the majority. Moreover, the machine perceptual characteristics are not effectively leveraged, leading to suboptimal compression efficiency. In this paper, we introduce Satisfied Machine Ratio (SMR) to address these issues. SMR statistically measures the quality of compressed images and videos for machines by aggregating satisfaction scores from them. Each score is calculated based on the difference in machine perceptions between original and compressed images. Targeting image classification and object detection tasks, we build two representative machine libraries for SMR annotation and construct a large-scale SMR dataset to facilitate SMR studies. We then propose an SMR prediction model based on the correlation between deep features differences and SMR. Furthermore, we introduce an auxiliary task to increase the prediction accuracy by predicting the SMR difference between two images in different quality levels. Extensive experiments demonstrate that using the SMR models significantly improves compression performance for VCM, and the SMR models generalize well to unseen machines, traditional and neural codecs, and datasets. In summary, SMR enables perceptual coding for machines and advances VCM from specificity to generality. Code is available at \url{https://github.com/ywwynm/SMR}

Algoritmo de estimação de movimento e sua arquitetura de hardware para HEVC

Doutoramento em Engenharia EletrotécnicaVideo coding has been used in applications like video surveillance, video conferencing, video streaming, video broadcasting and video storage. In a typical video coding standard, many algorithms are combined to compress a video. However, one of those algorithms, the motion estimation is the most complex task. Hence, it is necessary to implement this task in real time by using appropriate VLSI architectures. This thesis proposes a new fast motion estimation algorithm and its implementation in real time. The results show that the proposed algorithm and its motion estimation hardware architecture out performs the state of the art. The proposed architecture operates at a maximum operating frequency of 241.6 MHz and is able to process 1080p@60Hz with all possible variables block sizes specified in HEVC standard as well as with motion vector search range of up to ±64 pixels.A codificação de vídeo tem sido usada em aplicações tais como, vídeovigilância, vídeo-conferência, video streaming e armazenamento de vídeo. Numa norma de codificação de vídeo, diversos algoritmos são combinados para comprimir o vídeo. Contudo, um desses algoritmos, a estimação de movimento é a tarefa mais complexa. Por isso, é necessário implementar esta tarefa em tempo real usando arquiteturas de hardware apropriadas. Esta tese propõe um algoritmo de estimação de movimento rápido bem como a sua implementação em tempo real. Os resultados mostram que o algoritmo e a arquitetura de hardware propostos têm melhor desempenho que os existentes. A arquitetura proposta opera a uma frequência máxima de 241.6 MHz e é capaz de processar imagens de resolução 1080p@60Hz, com todos os tamanhos de blocos especificados na norma HEVC, bem como um domínio de pesquisa de vetores de movimento até ±64 pixels

Recent Developments in Video Surveillance

With surveillance cameras installed everywhere and continuously streaming thousands of hours of video, how can that huge amount of data be analyzed or even be useful? Is it possible to search those countless hours of videos for subjects or events of interest? Shouldn’t the presence of a car stopped at a railroad crossing trigger an alarm system to prevent a potential accident? In the chapters selected for this book, experts in video surveillance provide answers to these questions and other interesting problems, skillfully blending research experience with practical real life applications. Academic researchers will find a reliable compilation of relevant literature in addition to pointers to current advances in the field. Industry practitioners will find useful hints about state-of-the-art applications. The book also provides directions for open problems where further advances can be pursued

Fast motion estimation algorithm in H.264 standard

In H.264/AVC standard, the block motion estimation pattern is used to estimate the motion which is a very time consuming part. Although many fast algorithms have been proposed to reduce the huge calculation, the motion estimation time still cannot achieve the critical real time application. So to develop an algorithm which will be fast and having low complexity became a challenge in this standard.For this reasons, a lot of block motion estimation algorithms have been proposed. Typically the block motion estimation part is categorized into two parts. (1) Single pixel motion estimation (2) Fractional pixel motion estimation. In single pixel motion estimation one kind of fast motion algorithm uses fixed pattern like Three Step search, 2-D Logarithmic Search. Four Step search,Diamond Search, Hexagon Based Search. These algorithms are able to reduce the search point and get good coding quality. But the coding quality decreases when the fixed pattern does not fit the real life video sequence. In this thesis we tried to reduce the time complexity and number of search point by using an early termination method which is called adaptive threshold selection. We have used this method in three step search (TSS) and four step search and compared the performance with already existing block matching algorithm.This thesis work proposes fast sub-pixel motion estimation techniques having lower computational complexity. The proposed methods are based on mathematical models of the motion compensated prediction errors in compressing moving pictures. Unlike conventional hierarchical motion estimation techniques, the proposed methods avoid sub-pixel interpolation and subsequent secondary search after the integer-precision motion estimation, resulting in reduced computational time. In order to decide the coefficients of the models, the motion-compensated prediction errors of the neighboring pixels around the integer-pixel motion vector are utilized

A Research on Enhancing Reconstructed Frames in Video Codecs

A series of video codecs, combining encoder and decoder, have been developed to improve the human experience of video-on-demand: higher quality videos at lower bitrates. Despite being at the leading of the compression race, the High Efficiency Video Coding (HEVC or H.265), the latest Versatile Video Coding (VVC) standard, and compressive sensing (CS) are still suffering from lossy compression. Lossy compression algorithms approximate input signals by smaller file size but degrade reconstructed data, leaving space for further improvement. This work aims to develop hybrid codecs taking advantage of both state-of-the-art video coding technologies and deep learning techniques: traditional non-learning components will either be replaced or combined with various deep learning models. Note that related studies have not made the most of coding information, this work studies and utilizes more potential resources in both encoder and decoder for further improving different codecs.In the encoder, motion compensated prediction (MCP) is one of the key components that bring high compression ratios to video codecs. For enhancing the MCP performance, modern video codecs offer interpolation filters for fractional motions. However, these handcrafted fractional interpolation filters are designed on ideal signals, which limit the codecs in dealing with real-world video data. This proposal introduces a deep learning approach for all Luma and Chroma fractional pixels, aiming for more accurate motion compensation and coding efficiency.One extraordinary feature of CS compared to other codecs is that CS can recover multiple images at the decoder by applying various algorithms on the one and only coded data. Note that the related works have not made use of this property, this work enables a deep learning-based compressive sensing image enhancement framework using multiple reconstructed signals. Learning to enhance from multiple reconstructed images delivers a valuable mechanism for training deep neural networks while requiring no additional transmitted data.In the encoder and decoder of modern video coding standards, in-loop filters (ILF) dedicate the most important role in producing the final reconstructed image quality and compression rate. This work introduces a deep learning approach for improving the handcrafted ILF for modern video coding standards. We first utilize various coding resources and present novel deep learning-based ILF. Related works perform the rate-distortion-based ILF mode selection at the coding-tree-unit (CTU) level to further enhance the deep learning-based ILF, and the corresponding bits are encoded and transmitted to the decoder. In this work, we move towards a deeper approach: a reinforcement-learning based autonomous ILF mode selection scheme is presented, enabling the ability to adapt to different coding unit (CU) levels. Using this approach, we require no additional bits while ensuring the best image quality at local levels beyond the CTU level.While this research mainly targets improving the recent video coding standard VVC and the sparse-based CS, it is also flexibly designed to adapt the previous and future video coding standards with minor modifications.博士（工学）法政大学 (Hosei University

High-Performance Computing and Four-Dimensional Data Assimilation: The Impact on Future and Current Problems

This is the final technical report for the project entitled: "High-Performance Computing and Four-Dimensional Data Assimilation: The Impact on Future and Current Problems", funded at NPAC by the DAO at NASA/GSFC. First, the motivation for the project is given in the introductory section, followed by the executive summary of major accomplishments and the list of project-related publications. Detailed analysis and description of research results is given in subsequent chapters and in the Appendix

2018-2019 Lynn University Academic Catalog

https://spiral.lynn.edu/accatalogs/1046/thumbnail.jp

Proceedings of the Twenty-Third Annual Software Engineering Workshop

The Twenty-third Annual Software Engineering Workshop (SEW) provided 20 presentations designed to further the goals of the Software Engineering Laboratory (SEL) of the NASA-GSFC. The presentations were selected on their creativity. The sessions which were held on 2-3 of December 1998, centered on the SEL, Experimentation, Inspections, Fault Prediction, Verification and Validation, and Embedded Systems and Safety-Critical Systems

[Research Pertaining to Physics, Space Sciences, Computer Systems, Information Processing, and Control Systems]

Research project reports pertaining to physics, space sciences, computer systems, information processing, and control system