Image Data Compression Research Articles

A SENSOR AIDED H.264/AVC VIDEO ENCODER FOR AERIAL VIDEO SEQUENCES WITH IN THE LOOP METADATA CORRECTION

Abstract. Unmanned Aerial Vehicles (UAVs) are often employed to collect high resolution images in order to perform image mosaicking and/or 3D reconstruction. Images are usually stored on board and then processed with on-ground desktop software. In such a way the computational load, and hence the power consumption, is moved on ground, leaving on board only the task of storing data. Such an approach is important in the case of small multi-rotorcraft UAVs because of their low endurance due to the short battery life. Images can be stored on board with either still image or video data compression. Still image system are preferred when low frame rates are involved, because video coding systems are based on motion estimation and compensation algorithms which fail when the motion vectors are significantly long and when the overlapping between subsequent frames is very small. In this scenario, UAVs attitude and position metadata from the Inertial Navigation System (INS) can be employed to estimate global motion parameters without video analysis. A low complexity image analysis can be still performed in order to refine the motion field estimated using only the metadata. In this work, we propose to use this refinement step in order to improve the position and attitude estimation produced by the navigation system in order to maximize the encoder performance. Experiments are performed on both simulated and real world video sequences.

Dynamic CT perfusion image data compression for efficient parallel processing.

The increasing size of medical imaging data, in particular time series such as CT perfusion (CTP), requires new and fast approaches to deliver timely results for acute care. Cloud architectures based on graphics processing units (GPUs) can provide the processing capacity required for delivering fast results. However, the size of CTP datasets makes transfers to cloud infrastructures time-consuming and therefore not suitable in acute situations. To reduce this transfer time, this work proposes a fast and lossless compression algorithm for CTP data. The algorithm exploits redundancies in the temporal dimension and keeps random read-only access to the image elements directly from the compressed data on the GPU. To the best of our knowledge, this is the first work to present a GPU-ready method for medical image compression with random access to the image elements from the compressed data.

Onboard joint nondestructive despeckling and wavelet-based data compression of multilook synthetic aperture radar images using reordered spatial oriented trees

The suggested method of joint despeckling and set partitioning in hierarchical trees–based compression for degraded multilook synthetic aperture radar (SAR) images uses a fused image as a reference during compression. The fused image itself is formed from intermediate frames (looks) by the logical operation on wavelet coefficients, which are presented in the form of spatial oriented trees (SOTs) of wavelet decomposition. This decomposition is calculated for one and the same region of the Earth’s surface during SAR spacecraft flight. The high-frequency subbands (details) of the fused images contain the smallest wavelet coefficients from different SOTs. The wavelet coefficients related to the low-frequency subband (approximation) are processed by another special logical operation providing a good smoothing. Based on the improved fused image, the best bit rate (optimal operation point) is found by observing the output mean square errors and cross-correlation coefficients between the compressed and reference images. The results of computer modeling and comparison with a few well-known despeckling and compression procedures have shown the superb quality of the proposed method in the sense of different criteria such as peak signal-to-noise ratio, structural similarity, etc.

Reconfigurable architecture for real-time image compression on-board satellites

Data products of optical remote sensing systems are increasingly used in many areas of our everyday life. The spatial as well as the spectral resolution of satellite image data increases steadily with new missions resulting in a higher precision of known procedures and new application scenarios. While the memory capacity requirements can still be fulfilled, the transmission capacity becomes increasingly problematic. Real-time transmission of high-resolution image data is currently not possible. This thesis presents a new image data compression architecture that can be used for current and future projects at the German Aerospace Center (DLR). The architecture has region-of-interest support and offers flexible access to the compressed data based on the CCSDS 122.0-B-1 image data compression standard. Region of interest (ROI) coding can be useful in scenarios where on-board classification, registration, or object or change detection algorithms are used. It is also useful to decrease the amount of data that must be transferred to the ground station. Modifications to the standard have been made to permit a change of compression parameters and the re-organization of the bit-stream after compression. An additional index of the compressed data is created, which makes it possible to locate individual parts of the bit-stream. On request, compressed and stored images can be re-assembled and transmitted according to the application's needs and as requested by the ground station. The requirements, the design of the architecture, and its implementation based on reconfigurable hardware are presented. The architecture was developed for a spacequalified Xilinx Virtex 5QV, where a single instance of the architecture is capable of compressing images at a rate of up to 200 Mpx/s (or 400 Mbyte/s for 16 bit images). It operates at a clock frequency of 100MHz and processes two image pixels per clock cycle. A Xilinx Virtex-5QV enables thereby compressing images with a width of up to 4096 pixels without the use of external memory. Without external memory and additional interfaces, the power consumption of the architecture is about 5 W. The proposed architecture is one of the fastest implementations yet reported and sufficient for recent high-resolution systems. Investigations in the resource and power consumption, as well as the availability of external storage have shown that it should be possible to integrate the design directly on a focal plane.

Image data compression with hierarchical pixel averaging and fully adaptive prediction error coder

The fully adaptive prediction error coder (FAPEC) is an entropy coder that typically offers better results than the adaptive Rice compressor. It uses basic preprocessing stages such as delta preprocessing, but it can also be combined with a discrete wavelet transform. We describe a new algorithm called hierarchical pixel averaging (HPA). It divides an image into blocks of 16 x 16 pixels, which are subsequently divided into smaller blocks, up to the basic level where one block corresponds to one pixel. Average pixel values are determined for each level from which differential coefficients are extracted. HPA allows the introduction of controlled losses with several quality levels, also allowing to progressively decompress a given image from lower to higher quality. It achieves better resolution in sharp image edges when compared to other lossy algorithms. HPA is based on simple arithmetic operations, allowing a very simple (thus quick) implementation. It does not use any floating-point operations, which is an interesting feature for satellite or embedded data compression. We present a first implementation of HPA and the results obtained on a variety of images, both for the lossless and lossy cases with different quality levels. Our results indicate that HPA + FAPEC offer a performance comparable to that of CCSDS 122.0. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

Astronomical context coder for image compression

Recent lossless still image compression formats are powerful tools for compression of all kind of common images (pictures, text, schemes, etc.). Generally, the performance of a compression algorithm depends on its ability to anticipate the image function of the processed image. In other words, a compression algorithm to be successful, it has to take perfectly the advantage of coded image properties. Astronomical data form a special class of images and they have, among general image properties, also some specific characteristics which are unique. If a new coder is able to correctly use the knowledge of these special properties it should lead to its superior performance on this specific class of images at least in terms of the compression ratio. In this work, the novel lossless astronomical image data compression method will be presented. The achievable compression ratio of this new coder will be compared to theoretical lossless compression limit and also to the recent compression standards of the astronomy and general multimedia.

Lossless colour image compression using RCT for bi-level BWCA

A novel lossless colour image compression scheme based on a reversible colour transform (RCT) and Burrows–Wheeler compression algorithm (BWCA) is presented. The lossless transformation from RGB to YUV colour space provides highly correlated pixel intensities in the transformed image, thus aiding in higher compression. The proposed scheme uses a two-pass Burrows–Wheeler transform (BWT) for the individual source image colour planes to enhance grey-level homogeneity in the 2-D space. Compression efficiency is compared against various schemes including the JPEG 2000 lossless compression scheme and the previously developed kernel move-to-front transform-based BWCA (kernel BWCA). Validation is carried out via small- and large-size images. The proposed method using RCT with bi-level BWT results in better compression by taking advantage of the redundancy in the grey levels brought by the YUV colour space. For small-size images, it achieves 45 and 126 per cent more compression than the JPEG2000 lossless and kernel BWCA scheme, respectively. Among the different schemes compared, the proposed scheme achieves overall best performance and is well suited to small- and large-size image data compression.

Run-Length Encoding Graphic Rules Applied to DNA-Coded Images and Animation Editable by Polymerase Chain Reactions

We previously proposed novel designs for artificial genes as media for storing digitally compressed image data, specifically for biocomputing by analogy to natural genes mainly used to encode proteins. A run-length encoding (RLE) rule had been applied in DNA-based image data processing, to form coding regions, and noncoding regions were created as space for designing biochemical editing. In the present study, we apply the RLE-based image-coding rule to creation of DNAbased animation. This article consisted of three parts: (i) a theoretical review of RLE-based image coding by DNA, (ii) a technical proposal for biochemical editing of DNA-coded images using the polymerase chain reaction, and (iii) a minimal demonstration of DNAbased animation using simple model images encoded on short DNA molecules.

Total variation regularization via continuation to recover compressed hyperspectral images.

In this paper, we investigate a low-complexity scheme for decoding compressed hyperspectral image data. We have exploited the simplicity of the subgradient method by modifying a total variation-based regularization problem to include a residual constraint, employing convex optimality conditions to provide equivalency between the original and reformed problem statements. A scheme that utilizes spectral smoothness by calculating informed starting points to improve the rate of convergence is introduced. We conduct numerical experiments, using both synthetic and real hyperspectral data, to demonstrate the effectiveness of the reconstruction algorithm and the validity of our method for exploiting spectral smoothness. Evidence from these experiments suggests that the proposed methods have the potential to improve the quality and run times of the future compressed hyperspectral image reconstructions.

Social images tag ranking based on visual words in compressed domain

With the introduction of many image compression standards, the social images are stored and transmitted in compressed formats such as JPEG. For large-scale image database, tag ranking must fully decompress the compressed data to predict tag relevance based on visual content. In order to improve the accuracy of tag ranking and further reduce the ranking time, social images tag ranking based on visual words in compressed domain is proposed in this paper, which includes three steps: (1) low-resolution social images are constructed from the compressed image data; (2) visual words are created according to extracted SIFT descriptors in low-resolution social image; (3) the neighbor voting model is utilized to rank the image tags after matching the similarity based on visual words of an image. In order to evaluate the performance of the proposed method, average NDCG (normalized discounted cumulative gain) and tag ranking time are compared. Experimental results show that the proposed method can significantly reduce the time of image tag ranking under ensuring the ranking accuracy of social image tags.

Quad Tree Decomposition Based Analysis of Compressed Image Data Communication for Lossy and Lossless Using WSN

Quad Tree Decomposition Based Analysis of Compressed Image Data Communication for Lossy and Lossless Using WSN

Compressive scanning of an object signature

In this paper we explore the utility of compressive sensing for object signature generation in the optical domain. In the data acquisition stage we use laser scanning to obtain a small (sub-Nyquist) number of points of an object's boundary. This is used to construct the signature, thereby enabling object identification, reconstruction, and, image data compression. We refer to this framework as compressive scanning of objects' signatures. The main contributions of the paper are the following: (1) we use this framework to replace parts of the digital processing with optical processing and present one possible implementation, (2) the use of compressive scanning reduces laser data obtained and maintains high reconstruction accuracy, and (3) we show that using compressive sensing can lead to a reduction in the amount of stored data without significantly affecting the utility of this data for image recognition and image compression.

Fractal Image Coding Based on Wavelet Transformation

The classic problem of the existence of fractal coding time is too long, a kind of fast encoding algorithm was proposed in this paper, which is based on Wavelet and Fractal combined, using wavelet decomposition characteristics. This method reduces the amount of image data compression effectively, shorts coding time and improve the image encoding quality.

A new algorithm of image compression and encryption based on spatiotemporal cross chaotic system

This paper proposes a new image compression and encryption scheme based on the nearest-neighboring coupled-map lattices (NCML) and Non-uniform Discrete Cosine Transform (NDCT). A new cross chaotic map proposed based on Devaney's theory is used as the local map of NCML, which is called spatiotemporal cross chaotic system. The algorithm adopts Huffman coding and NDCT which carries out transformation of image data to compress image data. In this system, there are two layers of encryption protection. The compression data are packed into blocks, and permutation between blocks and diffusion in blocks are done simultaneously. The parameters produced by spatiotemporal cross chaotic system are used to control the non-uniformity of the NDCT, which has also played a role in encryption. The security test results indicate the proposed methods have high speed, high security and good compression effect.

Design of the JPEG2000 Compression System Based on ADV212

This article in detail on the basis of the algorithm of JPEG2000 standard upon, is put forward based on the FPGA embedded processor MicroBlaze and ADV212 compression chip with the combination of high speed, real time image compression scheme. Experiments show that the scheme can realize the data rate of 520 Mbps largest real-time compression of image data, when the image compression ratio is 53:1, reconstruction image PSNR value can reach 36 dB.

Text image compression based on statistical analysis and classification of the vertical line elements

A new original method for text data image compression is presented. Vertical line elements rather than connecting symbols of the text image are used as the main processing element. The given probability model quite accurately describes possible distortions of the vertical line elements, caused by printing and scanning noise. Based on the accepted probability model and statistical analysis methods, minimum most plausible set of undistorted elements in the entire set of the investigated vertical line elements is found. For each vertical element, the probability that this element is a distortion of an element of the set of undistorted line elements is found. Classification of the vertical line elements of the text image is based on a probabilistic assessment of the classified elements belonging to a single center. The end result of the proposed method is forming a dictionary of connecting symbols of the text image, where each class is represented by its most probable image and an allocation map of connecting symbols on the plane of the studied image. The proposed text image processing method has allowed to obtain a relatively high compression ratio with good quality of the reconstructed image. Comparison with the currently best special text image compression algorithm - JB2, within the format DjVu, has shown that the proposed algorithm has the advantage in data compression ratio of about 37% in processing a text page image with a resolution of 300 dpi.

SAR complex image data compression based on quadtree and zerotree Coding in Discrete Wavelet Transform Domain: A Comparative Study

Abstract A SAR complex image data compression algorithm based on quadtree coding (QC) in discrete wavelet transform (DWT) domain (QC-DWT) is proposed. We show that QC-DWT achieves the best performance for SAR complex image compression. Besides this, in this work, we observed a novel phenomenon that QC-DWT outperforms the zerotree based wavelet coding algorithms, e.g., Consultative Committee for Space Data Systems-Image Data Compression (CCSDS-IDC) and Set Partitioning in Hierarchical Trees algorithm (SPIHT) for SAR complex image data, and there exists deficiency of CCSDS-IDC for SAR complex image data compression. This is because the DWT coefficients of SAR complex image data always have intrascale clustering characteristic and no interscale attenuation characteristic, which is different from that of SAR amplitude images and other optical images.

Applied Technology in Image Retrieval via 8-Direction Gaussian Density in the DCT Domain

In this paper, A new algorithm for compressed image retrieval is proposed based on Gaussian Density Feature Vector(GDFV). This algorithm directly extract gaussian density of 8 direction from compressed image data to construct a 2-dimention array (8*4) as an indexing key to retrieve images based on their content features. To test and evaluate the proposed algorithms, we carried out experiments with a database of 1000 images. In comparison with existing representative techniques, the experimental results show the superiority of the proposed method in terms of retrieval precision and processing speed.

Effect of Lossy JPEG Compression of an Image with Chromatic Aberrations on Target Measurement Accuracy

Abstract. This paper reports an experiment conducted to investigate the effect of lossy JPEG compression of an image with chromatic aberrations on the measurement accuracy of target center by the intensity-weighted centroid method. I utilized six images shooting a white sheet with 30 by 20 black filled circles in the experiment. The images were acquired by a digital camera Canon EOS 20D. The image data were compressed by using two compression parameter sets of a downsampling ratio, a quantization table and a Huffman code table utilized in EOS 20D. The experiment results clearly indicate that lossy JPEG compression of an image with chromatic aberrations would produce a significant effect on measurement accuracy of target center by the intensity-weighted centroid method. The maximum displacements of red, green and blue components caused by lossy JPEG compression were 0.20, 0.09, and 0.20 pixels respectively. The results also suggest that the downsampling of the chrominance components Cb and Cr in lossy JPEG compression would produce displacements between uncompressed image data and compressed image data. In conclusion, since the author consider that it would be unable to correct displacements caused by lossy JPEG compression, the author would recommend that lossy JPEG compression before recording an image in a digital camera should not be executed in case of highly precise image measurement by using color images acquired by a non-metric digital camera.

EMD를 사용한 BTC 기반의 데이터 은닉

Block truncation coding (BTC) is a recent technique used for compression of monochrome image data. The original algorithm of BTC implement to use the standard mean and the standard deviation. For data hiding, it is needed to modify bit-planes of an images. These modifications yields unacceptable quality of BTC images. Thus, we propose IODBTC (Improved Ordered dithering BTC) improving BTC. In this paper, we improve this problem. Experimental results show that the reconstructed secret messages are the same as the original secret messages, and that the proposed scheme exhibits a good BTC images compared to that of previous schemes.