Tracking-Optimized Quantization for H.264 Compression in Transportation Video Surveillance Applications

We propose a tracking-aware system that removes video components of low tracking interest and optimizes the quantization during compression of frequency coefficients, particularly those that most influence trackers, significantly reducing bitrate while maintaining comparable tracking accuracy. We utilize tracking accuracy as our compression criterion in lieu of mean squared error metrics. The process of optimizing quantization tables suitable for automated tracking can be executed online or offline. The online implementation initializes the encoding procedure for a specific scene, but introduces delay. On the other hand, the offline procedure produces globally optimum quantization tables where the optimization occurs for a collection of video sequences. Our proposed system is designed with low processing power and memory requirements in mind, and as such can be deployed on remote nodes. Using H.264/AVC video coding and a commonly used state-of-the-art tracker we show that while maintaining comparable tracking accuracy our system allows for over 50% bitrate savings on top of existing savings from previous work

Learning Convolutional Networks for Content-weighted Image Compression

Lossy image compression is generally formulated as a joint rate-distortion optimization to learn encoder, quantizer, and decoder. However, the quantizer is non-differentiable, and discrete entropy estimation usually is required for rate control. These make it very challenging to develop a convolutional network (CNN)-based image compression system. In this paper, motivated by that the local information content is spatially variant in an image, we suggest that the bit rate of the different parts of the image should be adapted to local content. And the content aware bit rate is allocated under the guidance of a content-weighted importance map. Thus, the sum of the importance map can serve as a continuous alternative of discrete entropy estimation to control compression rate. And binarizer is adopted to quantize the output of encoder due to the binarization scheme is also directly defined by the importance map. Furthermore, a proxy function is introduced for binary operation in backward propagation to make it differentiable. Therefore, the encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner by using a subset of the ImageNet database. In low bit rate image compression, experiments show that our system significantly outperforms JPEG and JPEG 2000 by structural similarity (SSIM) index, and can produce the much better visual result with sharp edges, rich textures, and fewer artifacts

A content-aware quantisation mechanism for transform domain distributed video coding

The discrete cosine transform (DCT) is widely applied in modern codecs to remove spatial redundancies, with the resulting DCT coefficients being quantised to achieve compression as well as bit-rate control. In distributed video coding (DVC) architectures like DISCOVER, DCT coefficient quantisation is traditionally performed using predetermined quantisation matrices (QM), which means the compression is heavily dependent on the sequence being coded. This makes bit-rate control challenging, with the situation exacerbated in the coding of high resolution sequences due to QM scarcity and the non-uniform bit-rate gaps between them. This paper introduces a novel content-aware quantisation (CAQ) mechanism to overcome the limitations of existing quantisation methods in transform domain DVC. CAQ creates a frame-specific QM to reduce quantisation errors by analysing the distribution of DCT coefficients. In contrast to the predetermined QM that is applicable to only 4x4 block sizes, CAQ produces QM for larger block sizes to enhance compression at higher resolutions. This provides superior bit-rate control and better output quality by seeking to fully exploit the available bandwidth, which is especially beneficial in bandwidth constrained scenarios. In addition, CAQ generates superior perceptual results by innovatively applying different weightings to the DCT coefficients to reflect the human visual system. Experimental results corroborate that CAQ both quantitatively and qualitatively provides enhanced output quality in bandwidth limited scenarios, by consistently utilising over 90% of available bandwidth

aDORe djatoka: An Open-Source Jpeg 2000 Image Server and Dissemination Service Framework

4th International Conference on Open RepositoriesThis presentation was part of the session : Conference PresentationsDate: 2009-05-19 03:00 PM – 04:30 PMThe JPEG 2000 image format has attracted considerable attention due to its rich feature set defined in a multi-part open ISO standard, and its potential use as a holy-grail preservation format providing both lossless compression and rich service format features. Until recently there was lack of an implementation agnostic (e.g., Kakadu, Aware, etc) API for JPEG 2000 compression and extraction, and an open-source service framework, upon which rich Web 2.0-style applications can be developed. Recently we engaged in the development of aDORe djatoka , an open-source JPEG 2000 image server and dissemination framework to help address some of these issues. The djatoka image server is geared towards Web 2.0 style reuse through URI-addressability of all image disseminations including regions, rotations, and format transformations. Djatoka also provides a JPEG 2000 compression / extraction API that serves as an abstraction layer from the underlying JPEG 2000 library (e.g., Kakadu, Aware, etc).  The initial release has attracted considerable interest and is already being used in production environments, such as at the Biodiversity Heritage Library , who uses djatoka to serve more than eleven million images. This presentation introduces the aDORe djatoka image server and describes various interoperability approaches with existing repository systems.  Djatoka was derived from a concrete need to introduce a solution to disseminate high-resolution images stored in an aDORe repository system.  Djatoka is able to disseminate images that reside either in a repository environment or that are Web-accessible at arbitrary URIs.  Since dynamic service requests pertaining to an identified resource (the entire JPEG 2000 image) are being made, the OpenURL Framework was selected to provide an extensible dissemination service framework. The OpenURL service layer simplifies development and provides exciting interoperability opportunities. The presentation will showcase the flexibility of this interface by introducing a mobile image collection viewer developed for the iPhone / iTouch platform

Compression-Aware In-Memory Query Processing: Vision, System Design and Beyond

In-memory database systems have to keep base data as well as intermediate results generated during query processing in main memory. In addition, the effort to access intermediate results is equivalent to the effort to access the base data. Therefore, the optimization of intermediate results is interesting and has a high impact on the performance of the query execution. For this domain, we propose the continuous use of lightweight compression methods for intermediate results and have the aim of developing a balanced query processing approach based on compressed intermediate results. To minimize the overall query execution time, it is important to find a balance between the reduced transfer times and the increased computational effort. This paper provides an overview and presents a system design for our vision. Our system design addresses the challenge of integrating a large and evolving corpus of lightweight data compression algorithms in an in-memory column store. In detail, we present our model-driven approach and describe ongoing research topics to realize our compression-aware query processing vision

An NVM Aware MariaDB Database System and Associated IO Workload on File Systems

MariaDB is a community-developed fork of the MySQL relational database management system and originally designed and implemented in order to use the traditional spinning disk architecture. With Non-Volatile memory (NVM) technology now in the forefront and main stream for server storage (Data centers), MariaDB addresses the need by adding support for NVM devices and introduces NVM Compression method. NVM Compression is a novel hybrid technique that combines application level compression with flash awareness for optimal performance and storage efficiency. Utilizing new interface primitives exported by Flash Translation Layers (FTLs), we leverage the garbage collection available in flash devices to optimize the capacity management required by compression systems. We implement NVM Compression in the popular MariaDB database and use variants of commonly available POSIX file system interfaces to provide the extended FTL capabilities to the user space application. The experimental results show that the hybrid approach of NVM Compression can improve compression performance by 2-7x, deliver compression performance for flash devices that is within 5% of uncompressed performance, improve storage efficiency by 19% over legacy Row-Compression, reduce data writes by up to 4x when combined with other flash aware techniques such as Atomic Writes, and deliver further advantages in power efficiency and CPU utilization. Various micro benchmark measurement and findings on sparse files call for required improvement in file systems for handling of punch hole operations on files

Semantic-Aware Image Compressed Sensing

Deep learning based image compressed sensing (CS) has achieved great success. However, existing CS systems mainly adopt a fixed measurement matrix to images, ignoring the fact the optimal measurement numbers and bases are different for different images. To further improve the sensing efficiency, we propose a novel semantic-aware image CS system. In our system, the encoder first uses a fixed number of base CS measurements to sense different images. According to the base CS results, the encoder then employs a policy network to analyze the semantic information in images and determines the measurement matrix for different image areas. At the decoder side, a semantic-aware initial reconstruction network is developed to deal with the changes of measurement matrices used at the encoder. A rate-distortion training loss is further introduced to dynamically adjust the average compression ratio for the semantic-aware CS system and the policy network is trained jointly with the encoder and the decoder in an en-to-end manner by using some proxy functions. Numerical results show that the proposed semantic-aware image CS system is superior to the traditional ones with fixed measurement matrices.Comment: Modified versio

Preparation of Fuel Cell Stack Based on Fision Membrane

Preparation of single fuel cell stack using fision membrane of local materials has been done. In this research, beside preparation of fuel cell stack, the engineering side in the preparation of fuel cell stack also explored. The crucial steps of this research were on preparation of Membrane Electrode Assembly (MEA), stack construction, and flow field design used to distribute the fuel of hydrogen gas. The first two cases will be explained technically, but the third case, flow field design, will just be explained conceptually. In the preparation of MEA, in which fision membrane and Gas Diffusion Electrode (GDE) were assemblied, the compression factor must be noticed. Too high compression could caused crack in the GDE. However, low compression will result in a gap between membrane and GDE which could interfere the electron flow in the system and reduce the stack performance. At the stack construction, distribution of compression and leakage have also to be aware. These two factors could reduced the fuel cell stack performance drastically