“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.

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Image retrieval: Ideas, influences, and trends of the new age

The paper discusses the theory behind the dual-tree transform, shows how complex wavelets with good properties can be designed, and illustrates a range of applications in signal and image processing The authors use the complex number symbol C in CWT to avoid confusion with the often-used acronym CWT for the (different) continuous wavelet transform The four fundamentals, intertwined shortcomings of wavelet transform and some solutions are also discussed Several methods for filter design are described for dual-tree CWT that demonstrates with relatively short filters, an effective invertible approximately analytic wavelet transform can indeed be implemented using the dual-tree approach

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The dual-tree complex wavelet transform

https://www.cs.montana.edu/courses/csci566/Ref/WMSN-Survey.pdf

A survey on wireless multimedia sensor networks

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

Throughout this article, we concentrate on the transcoding of block-based video coding schemes that use hybrid discrete cosine transform (DCT) and motion compensation (MC). In such schemes, the frames of the video sequence are divided into macroblocks (MBs), where each MB typically consists of a luminance block (e.g., of size 16 /spl times/ 16, or alternatively, four 8 /spl times/ 8 blocks) along with corresponding chrominance blocks (e.g., 8 /spl times/ 8 Cb and 8 /spl times/ 8 Cr). This article emphasizes the processing that is done on the luminance components of the video. In general, the chrominance components can be handled similarly and will not be discussed in this article. We first provide an overview of the techniques used for bit-rate reduction and the corresponding architectures that have been proposed. Then, we describe the advances regarding spatial and temporal resolution reduction techniques and architectures. Additionally, an overview of error resilient transcoding is also provided, as well as a discussion of scalable coding techniques and how they relate to video transcoding. Finally, the article ends with concluding remarks, including pointers to other works on video transcoding that have not been covered in this article, as well as some future directions.

Video transcoding architectures and techniques: an overview

Significant improvements in video compression capability have been demonstrated with the introduction of the H.264/MPEG-4 advanced video coding (AVC) standard. Since developing this standard, the Joint Video Team of the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG) has also standardized an extension of that technology that is referred to as multiview video coding (MVC). MVC provides a compact representation for multiple views of a video scene, such as multiple synchronized video cameras. Stereo-paired video for 3-D viewing is an important special case of MVC. The standard enables inter-view prediction to improve compression capability, as well as supporting ordinary temporal and spatial prediction. It also supports backward compatibility with existing legacy systems by structuring the MVC bitstream to include a compatible “base view.” Each other view is encoded at the same picture resolution as the base view. In recognition of its high-quality encoding capability and support for backward compatibility, the stereo high profile of the MVC extension was selected by the Blu-Ray Disc Association as the coding format for 3-D video with high-definition resolution. This paper provides an overview of the algorithmic design used for extending H.264/MPEG-4 AVC towards MVC. The basic approach of MVC for enabling inter-view prediction and view scalability in the context of H.264/MPEG-4 AVC is reviewed. Related supplemental enhancement information (SEI) metadata is also described. Various “frame compatible” approaches for support of stereo-view video as an alternative to MVC are also discussed. A summary of the coding performance achieved by MVC for both stereo- and multiview video is also provided. Future directions and challenges related to 3-D video are also briefly discussed.

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Overview of the Stereo and Multiview Video Coding Extensions of the H.264/MPEG-4 AVC Standard

This paper describes extensions to the High Efficiency Video Coding (HEVC) standard that are active areas of current development in the relevant international standardization committees. While the first version of HEVC is sufficient to cover a wide range of applications, needs for enhancing the standard in several ways have been identified, including work on range extensions for color format and bit depth enhancement, embedded-bitstream scalability, and 3D video. The standardization of extensions in each of these areas will be completed in 2014, and further work is also planned. The design for these extensions represents the latest state of the art for video coding and its applications.

Standardized Extensions of High Efficiency Video Coding (HEVC)

The High Efficiency Video Coding (HEVC) standard has recently been extended to support efficient representation of multiview video and depth-based 3D video formats. The multiview extension, MV-HEVC, allows efficient coding of multiple camera views and associated auxiliary pictures, and can be implemented by reusing single-layer decoders without changing the block-level processing modules since block-level syntax and decoding processes remain unchanged. Bit rate savings compared with HEVC simulcast are achieved by enabling the use of inter-view references in motion-compensated prediction. The more advanced 3D video extension, 3D-HEVC, targets a coded representation consisting of multiple views and associated depth maps, as required for generating additional intermediate views in advanced 3D displays. Additional bit rate reduction compared with MV-HEVC is achieved by specifying new block-level video coding tools, which explicitly exploit statistical dependencies between video texture and depth and specifically adapt to the properties of depth maps. The technical concepts and features of both extensions are presented in this paper.

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Overview of the Multiview and 3D Extensions of High Efficiency Video Coding

This paper describes an algorithm which can achieve a constant bit rate when coding multiple video objects. The implementation is a nontrivial extension of the MPEG-4 rate control algorithm for single video objects which employs a quadratic rate quantizer model. The algorithm is organized into two stages: a pre- and a post-encoding stage. In the pre-encoding stage, an initial target estimate is made for each object. Based on the buffer fullness, the total target is adjusted and then distributed proportional to the relative size, motion, and variance of each object. Based on the new individual targets and rate-quantizer relation for texture, appropriate quantization parameters are calculated. After each object is encoded, the model parameters for each object are updated, and if necessary, frames are skipped to ensure that the buffer does not overflow. A preframeskip control is exercised to avoid buffer overflow when the motion and shape information occupies a significant portion of the bit budget. The rate control algorithm switches between two operation modes so that the coder can reduce the spatial coding accuracy for an improved temporal resolution. A shape-coding control mechanism is also proposed, which provides a tradeoff between texture and shape coding accuracy. Overall, the algorithm is able to successfully achieve the target bit rate, effectively code arbitrarily shaped objects, and maintain a stable buffer level. These techniques have been adopted by the MPEG committee in July 1997 as part of the video verification model (VM8).

Anthony Vetro

Papers

Video transcoding architectures and techniques: an overview

Overview of the Stereo and Multiview Video Coding Extensions of the H.264/MPEG-4 AVC Standard

Standardized Extensions of High Efficiency Video Coding (HEVC)

Overview of the Multiview and 3D Extensions of High Efficiency Video Coding

MPEG-4 rate control for multiple video objects