Prior-Information-Based Remote Sensing Image Compression with Bayesian Dictionary Learning

In this paper, we propose a new Light Field Image (LFI) compression scheme based on Multiview Video plus Depth (MVD) coding architecture. Through LF function analysis, we preliminarily estimate depth map according to the concept of Epipolar Plane Image (EPI). Such a rough estimation causes some error pixels within initial depth map, so we design a weighting mean filter to smooth the inaccurate region. The final estimated depth maps can be encoded by MVD coding standard jointly with a small number of viewpoint images in LFI, so as to improve compression efficiency of LFI. Ultimately, massive experiments are conducted on 4 LFIs to verify the effectiveness of the proposed compression scheme. The simulated results demonstrate that our LFI compression scheme can achieve a high LFI compression performance and outperform the-state-of-art coding solution.

Color image compression and encryption scheme based on compressive sensing and double random encryption strategy

The growing demand for visual telecommunication services over the last decade has greatly increased the need for efficient image coding and compression schemes. The work presented in this thesis examines several aspects of the problem of coding and compressing the various image and video-based services which are likely to utilize Broadband Integrated Services Digital Networks (BISDNs) in the future. This research has two major thrusts. The first is the development of a general-purpose, high-performance and high-quality image coding and compression scheme for these broadband-based visual services. The second is the development of an accurate traffic source modelling scheme for the Variable BitRate (VBR) packet video traffic produced by the proposed coding scheme. The proposed high-performance visual coding and compression scheme combines both statistical and psychovisual coding techniques to produce an optimum scheme which removes both statistical and psychovisual redundancies from images in the coding process. When used to encode four standard 512x512x8-bit test images, this scheme results in sub-distortion compression ratios of up to 27:1 without the use of any form of interframe coding. An efficient and flexible Asynchronous Transfer Mode (ATM) cell packaging scheme is also developed which allows the production of either 'priority' or 'non-priority' cell streams suitable for transmission over broadband networks. Three suitable VBR packet video traffic source models are developed during this study. Each of these sources reproduces the 'low-level' cell generation process and switches between different 'modes' of cell generation in order to capture the inherent `burstiness' and 'variability' of typical VBR packet video traffic streams. However, the way in which this cell production process is modelled, as well as the traffic levels which can be reproduced, differs for each of these sources. A relatively complex, general-purpose traffic source is proposed which is based on the hidden Markov statistical model and is able to model all levels of VBR traffic (up to 20 Mbps). A simpler 'switched-fractal' source is also proposed as an accurate model for the low (below 5 Mbps) VBR traffic only. The third proposed source is a 'switched-Markov' model which specifically caters for the high (5 to 20 Mbps) levels of VBR traffic. Among these three artificial traffic sources, the characteristics of all levels of traffic produced by the proposed video and image coding scheme are able to be reproduced accurately.

In this paper, we propose novel progressive scalable region of interest (ROI) image compression schemes with rate, distortion, and complexity trade-off based on vector quantization. The proposed schemes are unbalanced in the sense that the decoder has less computational requirements than the encoder. Moreover, the proposed method provides an interactive tool for fast ROI based browsing from image archives. Residual vector quantization (RVQ) equips the encoder with a multi-resolution apparatus which is useful for rate-distortion trade-of. Having all the advantages of RVQ, jointly sub-optimized RVQ provides a distortion-complexity adjustment. Since the bitstreams associated with the earlier stages of the RVQ encoder are more important for the reconstructed image quality than that of the later stages, an unequal error protection scheme, base on Reed-Solomon codes, has been considered to protect the transmitted bitstream against channel noise.

In this paper, a novel detection and recovery system is proposed for image authentication based on two compression schemes: block truncation coding and vector quantization. In order to reduce the overhead in the communication, the sender first compresses an image and then refines the characteristic values from the compressed image. The characteristic values are encrypted to digital signatures using the RSA scheme (proposed by Rivest, Shamir and Adleman). The signatures are embedded into the compressed image and thus the image integrity can be authenticated after transmission. Upon receipt of the compressed image, the receiver extracts the signatures and then decrypts the characteristic values using the sender's public key. Another characteristic value can also be decoded by the image compression scheme. By comparing a few characteristic values, the system determines whether or not the decompressed image is tampered and then outputs a correct characteristic value to rebuild the tampered regions. Furthermore, the proposed scheme authenticates the integrity of the image to the receiver and effectively recovers the tampered image.

This paper proposes a novel image compression scheme based on the local feature descriptor - Scale Invariant Feature Transform (SIFT). The SIFT descriptor characterizes an image region invariantly to scale and rotation. It is used widely in image retrieval. By using SIFT descriptors, our compression scheme is able to make use of external image contents to reduce visual redundancy among images. The proposed encoder compresses an input image by SIFT descriptors rather than pixel values. It separates the SIFT descriptors of the image into two groups, a visual description which is a significantly sub sampled image with key SIFT descriptors embedded and a set of differential SIFT descriptors, to reduce the coding bits. The corresponding decoder generates the SIFT descriptors from the visual description and the differential set. The SIFT descriptors are used in our SIFT-based matching to retrieve the candidate predictive patches from a large image dataset. These candidate patches are then integrated into the visual description, presenting the final reconstructed images. Our preliminary but promising results demonstrate the effectiveness of our proposed image coding scheme towards perceptual quality. Our proposed image compression scheme provides a feasible approach to make use of the visual correlation among images.

Medical Big Data (MBD) is the most critical form of information. MBD over a colossal Wireless Network (WN) is a sophisticated area of concern. Every day WN is becoming more dense and complicated. The MBD can be saved to wireless network after applying suitable Wavelet Compression. The quality of MBD is the most important factor; which means efficient and reliable transmission. Wavelet Compression ensures the best image compression schemes such that the quality of data is not affected. MBD is in the form of medical reports, x-rays, CT scan image, audio video files. MBD over WN needs limited bandwidth utilization. Software Define Network (SDN) divides the network infrastructure into two planes. The Control Plane manages data flow and the Data Plane deals with the data. In-network coordinating cache via SDN support is a better way of network efficiency improvement. The cache size is very limited and can entertain only a limited number of requests. Our proposed system for MBD Cache (MBDC) is efficient to improvement the network performance using Wavelets. It is capable to work in Heterogeneous Network (Het Net) and Software Define Network (SDN) Architecture. By considering any shortest path like K-Means algorithm, the neighboring-content cache awareness reduces the latency time to fulfill the request from destination.

A lossy compression scheme for Bayer images is proposed in this paper. Recently, it was found that compression-first schemes outperform the conventional demosaicking-first schemes in terms of output image quality. Balanced multiwavelet packet transforms effectively remove CFA image correlation between frequency bands. Wavelet coefficients shuffling exploring subband correlation makes it suitable for zerotree coding. Improved SPIHT algorithm further exploits data correlation in different direction under the same resolution using one symbol to denote three zerotrees while the SPIHT algorithm using three symbols. The simulation results show that: proposed compression scheme at low bit rate compared with JPEG2000 has better performance, and low complexity. CPSNR is 2db higher than JPEG2000 at bit rate 1 and is 0.7db higher than JPEG2000 at bit rate 2.

Compression schemes for digital audio, video and images are necessary to accommodate the increasing digital data traffic. Desirable features of such compression schemes are good perceptual quality, flexibility, higher compression ratios and fast decoding time. This paper focuses on low bit rate compression of digital images. We propose an incomplete orthogonal polynomial transform that can be implemented by a multiplication-free architecture. A subband coder is employed to quantize the transform domain coefficients so that the spatial correlation between the coefficients of the neighboring blocks can be exploited. The proposed approach improves the perceptual quality and the rate-distortion performance.

Most recently, there has been a growing interest in the study of dictionary learning based (DL-based) image compression, which has potential in relieving the bandwith-hungry bottleneck of visual communication. All existing DL-based image compression approaches mainly focus on the effective representation of images, thus losing sight of two basic elements of image compression, i.e., quantization and entropy coding. For this reason, this paper proposes a quantization and entropy coding scheme for DL-based image compression. In our scheme, the proposed Partition-Interval K-means (PIK) quantizer adaptively maps continuous coefficients to discrete values. The arithmetic coding, combined with differential coding technique, is applied to encode the indices of nonzero coefficients as well as the labels of quantization values. In our experiments, the proposed scheme is verified to be more effective than other quantization and entropy coding schemes for DL-based image compression.

Image compression is an important task for image transmission and storage. Thus far many compression techniques have been developed, such as transform image coding, predictive image coding, Vector Quantisation (VQ) and so on. Of these compression techniques, the techniques based on transform coding and VQ have received considerable attention. Recently, hybrid schemes for effective image compression have gained enormous popularity among researchers. In this paper, we analyse a hybrid scheme combining Kohonen’s Self-Organising Feature Map (SOFM)-based VQ coding and Set Partitioning In Hierarchical Trees (SPIHT) coding for compression of images (Rawat and Meher, 2009). The reconstructed image quality achieved after decoding depends upon the wavelet used in SPIHT. The effectiveness of the scheme for various orthogonal and biorthogonal wavelets for the hybrid scheme is tested in terms of Peak Signal-to-Noise Ratio (PSNR) and Visual Information Fidelity (VIF).

A remote sensing image encryption and compression scheme using novel hyperchaotic system and plaintext related random S-box

Wavelets are defined as mathematical functions that segment the data into different frequency levels. We can easily capture the fine and coarse details of an image or signal referred to as a subband. And it also helps in subband thresholding to achieve good compression performance. In recent days in telemedicine services, the handling of medical images is prominently increasing and it leads to the demand for medical image compression. While compressing the medical images, we have to concentrate on the data that holds important information, and at the same time, it must retain the image quality. Near-Lossless compression plays an essential role to achieve a better compression ratio than lossy compression and provides better quality than lossless compression. In this paper, we analyzed the sub-banding of Discrete Wavelet Transform (DWT) using different types of wavelets and made an optimal selection of wavelets for subband thresholding to attain a good compression performance with an application to medical images. We used Set Partitioning In Hierarchical Trees (SPIHT) compression scheme to test the compression performance of different wavelets. The Peak Signal to Noise Ratio (PSNR), Bits Per Pixel (BPP), Compression Ratio, and percentage of number of zeros are used as metrics to assess the performance of all the selected wavelets. And to find out its efficiency in possessing the essential information of medical images, the subband of the selected wavelets is further utilized to devise the near-lossless compression scheme for medical images.

Hybrid variable length coding (HVLC) was recently proposed as a novel entropy coding scheme for block-based image and video compression, which divides each transform block into low frequency (LF) region and high frequency (HF) region and codes them differently. To take advantage of the clustered nature of the nonzero coefficients in the LF region, a two-dimensional position and one- dimensional amplitude coding scheme (2DP1DA) was also proposed, which codes the 2D position information, i.e., run of consecutive zero-valued coefficients and run of consecutive nonzero coefficients, jointly by a 2D code table. To further improve the coding efficiency, joint position and amplitude coding is desirable. In this paper, we propose a three-dimensional position and amplitude coding scheme (3DPA) to jointly code the position and amplitude information for each nonzero cluster by a 3D code table. The experimental results show that HVLC with 3DPA for coding LF region achieves about 3.2%~3.7% bit rate reduction for a wide range of quantization parameters (QP) compared with CAVLC in H.264. Moreover, the 3DPA scheme is a compact way to realize the joint position and amplitude coding and it is very suitable for context-adaptive HVLC design.

Set Partitioning in Hierarchical Trees (SPIHT) is an effective image compression algorithm. SPIHT is a unique approach which transmits the values of the pixels indirectly to the coordinates. The packet coders in the images present a universal transform the framework in a constriction of the filter bank schema which is attained by the Space Frequency Quantization (SFQ) and the effective compression is achieved by the Huffman coding. The proposed scheme permits transformation in the joints and alterations in the discrete magnitude of the input image. Hence, coding performance is enhanced and the image is compressed without compromising the excellence of the image. MSPIHT is an enhanced algorithm that fits the requirement of medical image compression for storage and transmission of medical data. MSPIHT improves on the standard SPIHT in the encoding operation of wavelet coefficients to provide high quality images at low bit rates important in medical applications where accuracy is paramount. The modification is aimed at changing the set partitioning strategy by minimizing memory consumption rates, thus improving the compression ratio and speed. It also may be used at telemedicine where high image compression rate is required for the quick and efficient image transfer for diagnostics and consultations. Experimental results shows that the image compression scheme is improved with the SPIHT-SFQ based Huffman Coding scheme and it outperforms the current image compression algorithms.