Adaptive Compression Research Articles

Salient Object Detection via Low-Rank and Structured Sparse Matrix Decomposition

Salient object detection provides an alternative solution to various image semantic understanding tasks such as object recognition, adaptive compression and image retrieval. Recently, low-rank matrix recovery (LR) theory has been introduced into saliency detection, and achieves impressed results. However, the existing LR-based models neglect the underlying structure of images, and inevitably degrade the associated performance. In this paper, we propose a Low-rank and Structured sparse Matrix Decomposition (LSMD) model for salient object detection. In the model, a tree-structured sparsity-inducing norm regularization is firstly introduced to provide a hierarchical description of the image structure to ensure the completeness of the extracted salient object. The similarity of saliency values within the salient object is then guaranteed by the $\ell _\infty$-norm. Finally, high-level priors are integrated to guide the matrix decomposition and enhance the saliency detection. Experimental results on the largest public benchmark database show that our model outperforms existing LR-based approaches and other state-of-the-art methods, which verifies the effectiveness and robustness of the structure cues in our model.

Two-Dimensional Orthogonal DCT Expansion in Trapezoid and Triangular Blocks and Modified JPEG Image Compression

In the conventional JPEG algorithm, an image is divided into eight by eight blocks and then the 2-D DCT is applied to encode each block. In this paper, we find that, in addition to rectangular blocks, the 2-D DCT is also orthogonal in the trapezoid and triangular blocks. Therefore, instead of eight by eight blocks, we can generalize the JPEG algorithm and divide an image into trapezoid and triangular blocks according to the shapes of objects and achieve higher compression ratio. Compared with the existing shape adaptive compression algorithms, as we do not try to match the shape of each object exactly, the number of bytes used for encoding the edges can be less and the error caused from the high frequency component at the boundary can be avoided. The simulations show that, when the bit rate is fixed, our proposed algorithm can achieve higher PSNR than the JPEG algorithm and other shape adaptive algorithms. Furthermore, in addition to the 2-D DCT, we can also use our proposed method to generate the 2-D complete and orthogonal sine basis, Hartley basis, Walsh basis, and discrete polynomial basis in a trapezoid or a triangular block.

Salient Region Detection Improved by Principle Component Analysis and Boundary Information

Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. A novel method to determine the salient regions of images is proposed in this paper. The L₀ smoothing filter and principle component analysis (PCA) play important roles in our framework. The L₀ filter is extremely helpful in characterizing fundamental image constituents, i.e., salient edges, and can simultaneously diminish insignificant details, thus producing more accurate boundary information for background merging and boundary scoring. PCA can reduce computational complexity as well as attenuate noise and translation errors. A local-global contrast is then used to calculate the distinction. Finally, image segmentation is used to achieve full-resolution saliency maps. The proposed method is compared with other state-of-the-art saliency detection methods and shown to yield higher precision-recall rates and F-measures.

ADAPTIVE TCHEBICHEF MOMENT TRANSFORM IMAGE COMPRESSION USING PSYCHOVISUAL MODEL

An extension of the standard JPEG image compression known as JPEG-3 allows rescaling of the quantization matrix to achieve a certain image outp ut quality. Recently, Tchebichef Moment Transform (TMT) has been introduced in the field of image compression. TMT has been shown to perform better than the standard JPEG image compression. This study presents an adaptive TMT image compression. This task is obtained by generating custom quantization tables f or low, medium and high image output quality levels based on a psychovisual model. A psychovisual model is developed to approximate visual threshold on Tchebichef moment from image reconstruction error. The contribution of each moment will be investigated and analy zed in a quantitative experiment. The sensitivity of TM T basis functions can be measured by evaluating the ir contributions to image reconstruction for each mome nt order. The psychovisual threshold model allows a developer to design several custom TMT quantization tables for a user to choose from according to his or her target output preference. Consequently, these quant ization tables produce lower average bit length of Huffman code while still retaining higher image quality tha n the extended JPEG scaling scheme.

Adaptive visually lossless JPEG-based color image compression

The paper presents two approaches to adaptive JPEG-based compression of color images inside digital cameras. Compression for both approaches, although lossy, is organized in such a manner that introduced distortions are not visible. This is done taking into account quality of each original image before it is subject to lossy compression. Noise characteristics and blur are assumed to be the main factors determining visual quality of original images. They are estimated in a fast and blind (automatic) manner for images in RAW format (first approach) and in Bitmap (second approach). The dominant distorting factor which can be either noise or blur is determined. Then, the scaling factor (SF) of JPEG quantization table is adaptively adjusted to preserve valuable information in a compressed image with taking into account estimated noise and blur influence. The advantages and drawbacks of the proposed approaches are discussed. Both approaches are intensively tested for real-life images. It is demonstrated that the second approach provides more accurate estimate of degrading factor characteristics, and thus, a larger compression ratio (CR) increase compared to super-high quality (SHQ) mode used in consumer digital cameras. The first approach mainly relies on the prediction of noise and blur characteristics to be observed in Bitmap images after a set of nonlinear operations applied to RAW data in image processing chain. It is simpler and requires less memory but appeared to be slightly less beneficial. Both approaches are shown to provide, on the average, more than two times increase in average CR compared to SHQ mode without introducing visible distortions with respect to SHQ compressed images. This is proven by the analysis of modern visual quality metrics able to adequately characterize compressed image quality.

Adaptive Online Compression in Clouds—Making Informed Decisions in Virtual Machine Environments

Infrastructure as a Service clouds often use virtual machines to host different customers on the same physical hardware. This form of resource sharing can lead to unpredicatable performance degradations for the individual customer, especially with regard to data-intensive applications, which heavily depend on stable I/O characteristics. One traditional approach to cope with I/O fluctuations is adaptive online compression. In this paper we present a new scheme for adaptive online compression which has been explicitly designed to work in co-located virtual machine environments. In contrast to existing adaptive online compression schemes, the decision model of our approach does not rely on the system metrics CPU utilization and I/O bandwidth, which we demonstrate to be often displayed inaccurately inside XEN, KVM, and Amazon EC2-based virtual machines. Instead, it only considers the application data rate. Without requiring any calibration or training phase our adaptive compression scheme can improve the I/O throughput of virtual machines up to a factor of four as shown through extended experimental evaluations.

Interpolation-tuned salient region detection

Image salient region detection is very useful in many multimedia applications, such as image retrieval, adaptive content delivery, and adaptive compression. Most existing methods are based on center-surround differences, usually detecting the differences around region boundaries, so these methods emphasize the high-contrast edges instead of detecting the regions. Different from traditional methods, our method smoothly propagates salient region information to the whole image by performing color interpolation and produces such saliency maps that uniformly highlight the whole salient regions and have high contrast between salient regions and backgrounds. We compare our method with five state-of-the-art salient region detection methods on a large public data set. The proposed method not only generates visually reasonable results but also achieves higher precision and better recall rates.

Efficient DCT-based image compression technique

Although JPEG technique is considered as the most popular image compression standard, it behaves high visual degradation at low bit rates. In this paper, an efficient DCT–based image compression technique is proposed to achieve high Compression Ratio (CR) with high quality at both high and low bit rates. This technique uses switching between JPEG compression technique at high bit rates and a novel Adaptive Lossy Image Compression (ALIC) technique at low bit rates. ALIC is proposed to overcome the drawbacks of JPEG technique at low bitrates. The performance of the proposed technique is analysed at low and high bit rates on both grey and colour images. Performances of both JPEG and ALIC techniques are analysed and compared. The experimental results reveal that the proposed ALIC technique achieves better CR with acceptable SNR in comparison with JPEG technique. Also, the resultant CR of ALIC technique can be considerably increased with a slight decrease of its PSNR. This decrease in PSNR does not result in a noticeable visual degradation of the compressed image. On the other hand, increasing the CR of JPEG technique results in a noticeable visual degradation due to the appearance of blocking effect in the reconstructed image. Thus, it is greatly recommended to use ALIC technique in the applications that require high CR with stable PSNR. ALIC is a general purpose technique that can be applied, not only on images, but also on any data source which uses Huffman coding to achieve better CR. Therefore, it is suitable for compression of text, image and video.

Query Evaluation for XML Databases using Partial Decompression

This research work demonstrates the Extraction, compression and query processing of XML documents for Adaptive Compression Techniques and Efficient Query Evaluation. Proposed here are the algorithms for xml compression and Efficient Query Evaluation as - Feasible XML compression using data compression algorithm. Qurey Processor using Sax parsing and Interfaces. It is shown that using the proposed techniques for xml data compression will pave a way for better compression and improve the compression ratio and performance of compressor system

Power Efficient Adaptive Compression Technique for Wireless Sensor Networks

Wireless sensor network consists of many tiny disposable low power devices called nodes, which are spatially distributed in order to perform various application global tasks. These nodes form a network by communicating with each other either directly or through other nodes. Sensor networks are fundamentally constrained by the difficulty and energy expense of delivery information from sensor to sink. Power saving is a critical issue in wireless sensor networks since sensor nodes are powered by batteries which cannot generally change or recharged. In this paper we have focused on radio communication which is often considered as the main cause of energy consumption. One of the solutions to this problem would be generally achieved by reducing the size of the transmitted/received data through data compression. A new adaptive compression technique will be considered for lossless data compression which reduces the amount of data that must be passed through the network and to the sink and thus have energy benefits that are multiplicative with the number of hops the data travel through the network. Possible extensions of this paper, use RUN LENGTH CODING to compress decompress the data in WSN.

An Improved Technique to Compute Visual Attention Map based upon Wavelet Domain

Visual system of human beings does not process the complete area of image rather focus upon limited area of visual image. But in which area does the visual attention focused is a topic of hot research nowadays. Research on psychological phenomenon indicates that attention is attracted to features that differ from its surroundings or the one that are unusual or unfamiliar to the human visual system. Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. Object or region based image processing can be performed more efficiently with information pertaining locations that are visually salient to human perception with the aid of a saliency map. The saliency map is a master topological map having the possible locations of objects or regions which a human perceived as important/salient. In this paper a method for computing the saliency map in wavelet transform domain has been proposed. Earlier works in this domain although calculated the saliency very efficiently but they just involve the approximation components of DWM, the detail components have not been taken care but in many applications these detailed components are of great importance to highlight the smaller but visually important components.

Real-time bas-relief generation from a 3D mesh

Most of the existing approaches to bas-relief generation operate in image space, which is quite time-consuming in practice. This paper presents a different bas-relief generation algorithm based on geometric compression and starting from a 3D mesh input. The feature details are first extracted from the original objects using a spatial bilateral filtering technique. Then, a view-dependent coordinate mapping method is applied to build the height domain for the current view. After fitting the compression datum plane, the algorithm uses an adaptive compression function to scale and combine the Z values of the base mesh and the fine details. This approach offers control over the level of detail, making it flexible for the adjustment of the appearance of details. For a typical input mesh with 100k triangles, this algorithm computes a bas-relief in 0.214s.

A new data aggregation scheme via adaptive compression for wireless sensor networks

Data aggregation is necessary for extending the network lifetime of wireless sensor nodes with limited processing and power capabilities, since energy expended in transmitting a single data bit would be at least several orders of magnitude higher when compared to that needed for a 32-bit computation. Therefore, in this article, a novel nonlinear adaptive pulse coded modulation-based compression (NADPCMC) scheme is proposed for data aggregation in a wireless sensor network (WSN). The NADPCMC comprises of two estimators—one at the source or transmitter and the second one at the destination node. The estimator at the source node approximates the data value for each sample. The difference between the data sample and its estimate is quantized and transmitted to the next hop node instead of the actual data sample, thus reducing the amount of data transmission and rending energy savings. A similar estimator at the next hop node or base station reconstructs the original data. It is demonstrated that repeated application of the NADPCMC scheme along the route in a WSN results in data aggregation. Satisfactory performance of the proposed scheme in terms of distortion, compression ratio, and energy efficiency and in the presence of estimation and quantization errors for data aggregation is demonstrated using the Lyapunov approach. Then the performance of the proposed scheme is contrasted with the available compression schemes in an NS-2 environment through several benchmarking datasets. Simulation and hardware results demonstrate that almost 50% energy savings with low distortion levels below 5% and low overhead are observed when compared to no compression. Iteratively applying the proposed compression scheme at the cluster head nodes along the routes over the network yields an additional improvement of 20% in energy savings per aggregation with an overall distortion below 8%.

Research on adaptive compression coding for network coding in wireless sensor network

Based on the sequence entropy of Shannon information theory, we work on the network coding technology in Wireless Sensor Network (WSN). In this paper, we take into account the similarity of the transmission sequences at the network coding node in the multi-sources and multi-receivers network in order to compress the data redundancy. Theoretical analysis and computer simulation results show that this proposed scheme not only further improves the efficiency of network transmission and enhances the throughput of the network, but also reduces the energy consumption of sensor nodes and extends the network life cycle.

A new strategy for simulation of electrochemical mechanisms involving acute reaction fronts in solution under spherical or cylindrical diffusion

In this paper we present a combined simulation approach for solving complex electrochemical kinetic problems at (hemi)spherical and (hemi)cylindrical electrodes based on the simultaneous application of analytical conformal coordinate transformations to accurately treat the diffusional transport and adaptive grid compression to locally increase the accuracy of the approximation of acute reaction fronts. This strategy is shown to be very efficient and leads to extremely accurate results both for steady state and transient situations and is equally powerful for both pure diffusion and that complicated by severe kinetic distortions.

A Research on Adaptive Compressive Sensing Applied to Video Coding

A Research on Adaptive Compressive Sensing Applied to Video Coding

Trade-Off Analysis of Energy Consumption and Image Quality for Multihop Wireless Sensor Networks

It has become necessary in recent years to observe and monitor some physical phenomena. This was made possible by the emergence of wireless sensor networks. The main characteristic of such networks is nodes with scarce resources. Given the stringent resource constraints, nodes are limited in energy, memory and computational power. These resource constraints pose serious difficulties for image processing and transmission to the destination. Therefore, image transfer in wireless sensor networks presents major challenge which raises issues related to its representation, its storage and its transmission. Based on wavelet transform an Adaptive Energy Efficient Wavelet Image Compression Algorithm is proposed in order to be suitable for wireless sensor network. In addition, an identification of the wavelet image compression parameters is investigated to analyze the trade-offs between the energy saving, and the image quality. Performance studies indicate that the proposed scheme enabling significant reductions in computation as well as communication energy needed, with minimal degradation in image quality.

Adaptive Compression of Texture Pyramids

AbstractHigh‐quality texture minification techniques, including trilinear and anisotropic filtering, require texture data to be arranged into a collection of pre‐filtered texture maps called mipmaps. In this paper, we present a compression scheme for mipmapped textures which achieves much higher quality than current native schemes by exploiting image coherence across mipmap levels. The basic idea is to use a high‐quality native compressed format for the upper levels of the mipmap pyramid (to retain efficient minification filtering) together with a novel compact representation of the detail provided by the highest‐resolution mipmap. Key elements of our approach include delta‐encoding of the luminance signal, efficient encoding of coherent regions through texel runs following a Hilbert scan, a scheme for run encoding supporting fast random‐access, and a predictive approach for encoding indices of variable‐length blocks. We show that our scheme clearly outperforms native 6:1 compressed texture formats in terms of image quality while still providing real‐time rendering of trilinearly filtered textures.

Image Compression in Wireless sensor networks- A survey

Wireless Sensor networks are battery powered due to which their lifetime is precisely limited. In this type of network the nodes commonly have very limited resources in terms of processing power, bandwidth and energy. Efficient coding of the multimedia content is therefore important. One possible way of achieve maximum utilization of those resource is applying compression on sensor event Usually, processing data consumes much less power than transmitting data in wireless medium, so it is effective to apply compression before transmitting data for reducing total power consumption by a sensor node. In this paper various energy efficient image compression techniques Collaborative image transmission using Sobal edge-detection, JPEG2000 image compression, Image Subtraction with Quantization of image; Adaptive Compression and Spatial Correlation-Based Image Compression are discussed.

Adaptive compression against a countable alphabet

This paper sheds light on universal coding with respect to classes of memoryless sources over a countable alphabet defined by an envelope function with finite and non-decreasing hazard rate. We prove that the auto-censuring (AC) code introduced by Bontemps (2011) is adaptive with respect to the collection of such classes. The analysis builds on the tight characterization of universal redundancy rate in terms of metric entropy by Haussler and Opper (1997) and on a careful analysis of the performance of the AC-coding algorithm. The latter relies on non-asymptotic bounds for maxima of samples from discrete distributions with finite and non-decreasing hazard rate.