Line-based, reduced memory, wavelet image compression

SSSIC: Semantics-to-Signal Scalable Image Coding With Learned Structural Representations

We address the requirement of image coding for joint human-machine vision, i.e., the decoded image serves both human observation and machine analysis/understanding. Previously, human vision and machine vision have been extensively studied by image (signal) compression and (image) feature compression, respectively. Recently, for joint human-machine vision, several studies have been devoted to joint compression of images and features, but the correlation between images and features is still unclear. We identify the deep network as a powerful toolkit for generating structural image representations. From the perspective of information theory, the deep features of an image naturally form an entropy decreasing series: a scalable bitstream is achieved by compressing the features backward from a deeper layer to a shallower layer until culminating with the image signal. Moreover, we can obtain learned representations by training the deep network for a given semantic analysis task or multiple tasks and acquire deep features that are related to semantics. With the learned structural representations, we propose SSSIC, a framework to obtain an embedded bitstream that can be either partially decoded for semantic analysis or fully decoded for human vision. We implement an exemplar SSSIC scheme using coarse-to-fine image classification as the driven semantic analysis task. We also extend the scheme for object detection and instance segmentation tasks. The experimental results demonstrate the effectiveness of the proposed SSSIC framework and establish that the exemplar scheme achieves higher compression efficiency than separate compression of images and features.

Performance Analysis of Compression Techniques for Chronic Wound Image Transmission Under Smartphone-Enabled Tele-Wound Network

The healing status of chronic wounds is important for monitoring the condition of the wounds. This article designs and discusses the implementation of smartphone-enabled compression technique under a tele-wound network (TWN) system. Nowadays, there is a huge demand for memory and bandwidth savings for clinical data processing. Wound images are captured using a smartphone through a metadata application page. Then, they are compressed and sent to the telemedical hub with a set partitioning in hierarchical tree (SPIHT) compression algorithm. The transmitted image can then be reduced, followed by an improvement in the segmentation accuracy and sensitivity. Better wound healing treatment depends on segmentation and classification accuracy. The proposed framework is evaluated in terms of rates (bits per pixel), compression ratio, peak signal to noise ratio, transmission time, mean square error and diagnostic quality under telemedicine framework. A SPIHT compression technique assisted YDbDr-Fuzzy c-means clustering considerably reduces the execution time (105s), is simple to implement, saves memory (18 KB), improves segmentation accuracy (98.39%), and yields better results than the same without using SPIHT. The results favor the possibility of developing a practical smartphone-enabled telemedicine system and show the potential for being implemented in the field of clinical evaluation and the management of chronic wounds in the future.

A Survey of Image Compression Algorithms for Visual Sensor Networks

With the advent of visual sensor networks (VSNs), energy-aware compression algorithms have gained wide attention. That is, new strategies and mechanisms for power-efficient image compression algorithms are developed, since the application of the conventional methods is not always energy beneficial. In this paper, we provide a survey of image compression algorithms for visual sensor networks, ranging from the conventional standards such as JPEG and JPEG2000 to a new compression method, for example, compressive sensing. We provide the advantages and shortcomings of the application of these algorithms in VSN, a literature review of their application in VSN, as well as an open research issue for each compression standard/method. Moreover, factors influencing the design of compression algorithms in the context of VSN are presented. We conclude by some guidelines which concern the design of a compression method for VSN.

Video Compression Schemes Using Edge Feature on Wireless Video Sensor Networks

This paper puts forward a low-complexity video compression algorithm that uses the edges of objects in the frames to estimate and compensate for motion. Based on the proposed algorithm, two schemes that balance energy consumption among nodes in a cluster on a wireless video sensor network (WVSN) are proposed. In these schemes, we divide the compression process into several small processing components, which are then distributed to multiple nodes along a path from a source node to a cluster head in a cluster. We conduct extensive computational simulations to examine the truth of our method and find that the proposed schemes not only balance energy consumption of sensor nodes by sharing of the processing tasks but also improve the quality of decoding video by using edges of objects in the frames.

View compensated compression of volume rendered images for remote visualization

Remote visualization of volumetric images has gained importance over the past few years in medical and industrial applications. Volume visualization is a computationally intensive process, often requiring hardware acceleration to achieve a real time viewing experience. One remote visualization model that can accomplish this would transmit rendered images from a server, based on viewpoint requests from a client. For constrained server-client bandwidth, an efficient compression scheme is vital for transmitting high quality rendered images. In this paper, we present a new view compensation scheme that utilizes the geometric relationship between viewpoints to exploit the correlation between successive rendered images. The proposed method obviates motion estimation between rendered images, enabling significant reduction to the complexity of a compressor. Additionally, the view compensation scheme, in conjunction with JPEG2000 performs better than AVC, the state of the art video compression standard.

Reduced-complexity delayed-decision algorithm for context-based image processing systems

It is well known that the performance of context-based image processing systems can be improved by allowing the processor (e.g., an encoder or a denoiser) a delay of several samples before making a processing decision. Often, however, for such systems, traditional delayed-decision algorithms can become computationally prohibitive due to the growth in the size of the space of possible solutions. In this paper, we propose a reduced-complexity, one-pass, delayed-decision algorithm that systematically reduces the size of the search space, while also preserving its structure. In particular, we apply the proposed algorithm to two examples of adaptive context-based image processing systems, an image coding system that employs a context-based entropy coder, and a spatially adaptive image-denoising system. For these two types of widely used systems, we show that the proposed delayed-decision search algorithm outperforms instantaneous-decision algorithms with only a small increase in complexity. We also show that the performance of the proposed algorithm is better than that of other, higher complexity, delayed-decision algorithms.

Efficient architectures for two-dimensional discrete wavelet transform using lifting scheme

Novel architectures for 1-D and 2-D discrete wavelet transform (DWT) by using lifting schemes are presented in this paper. An embedded decimation technique is exploited to optimize the architecture for 1-D DWT, which is designed to receive an input and generate an output with the low- and high-frequency components of original data being available alternately. Based on this 1-D DWT architecture, an efficient line-based architecture for 2-D DWT is further proposed by employing parallel and pipeline techniques, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. This 2-D architecture is called fast architecture (FA) that can perform J levels of decomposition for N * N image in approximately 2N2(1 - 4(-J))/3 internal clock cycles. Moreover, another efficient generic line-based 2-D architecture is proposed by exploiting the parallelism among four subband transforms in lifting-based 2-D DWT, which can perform J levels of decomposition for N * N image in approximately N2(1 - 4(-J))/3 internal clock cycles; hence, it is called high-speed architecture. The throughput rate of the latter is increased by two times when comparing with the former 2-D architecture, but only less additional hardware cost is added. Compared with the works reported in previous literature, the proposed architectures for 2-D DWT are efficient alternatives in tradeoff among hardware cost, throughput rate, output latency and control complexity, etc.