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Histogram-based rate-distortion estimation for MPEG-2 video

IEE PROCEEDINGS-VISION image AND SIGNAL PROCESSING 1999年第4期146卷 198-205页

作者： Hong, SH Kim, SD LG Elect Inc DVA Team DTV Res Lab Seocho Gu Seoul 137724 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn Yusong Gu Taejon 305701 South Korea

A rate-distortion estimation method for MPEG-2 video that enables one to predict the number of bits and the distortion to be generated from an encoded picture at a given quantisation step size, and vice versa is proposed. The estimation method has some outstanding advantages: first, the computational complexity is small because its major operation is just to obtain a histogram or weighted histogram of the DCT coefficients from an input picture, and the final form of the proposed estimation model is simple. Secondly, its results are accurate enough to be applied to practical video coding applications. Simulation results show that the estimation errors for the rate and for the distortion do not exceed 2.5% and 1%, respectively.

关键词： rate distortion theory code standards computational complexity video coding video coding applications telecommunication standards MPEG-2 video data compression quantisation step size transform coding encoded picture input picture weighted histogram video signal processing quantisation (signal) discrete cosine transforms histogram-based rate-distortion estimation Integral transforms in numerical analysis DCT coefficients estimation errors image and video coding simulation results

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An orthogonal polynomials transform-based variable block size adaptive vector quantisation for colour image coding

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IET image PROCESSING 2012年第6期6卷 635-646页

作者： Krishnamoorthy, R. Punidha, R. Anna Univ Technol Comp Vis Lab Dept CSE Tiruchirappalli 620024 Tamil Nadu India

In this paper, a new adaptive vector quantisation with variable block size based on orthogonal polynomials is proposed for colour image coding. The input colour image is partitioned into fixed larger block sizes and the homogeneity/complexity of the local region is tested with the mean-normalised variance classifier. The homogeneous regions are encoded with fixed larger blocks and the complex regions are further divided into smaller block sizes for encoding as it gives the wealth of low-level information such as the high detail and high number of contrast edges. The integer-based orthogonal polynomials transform is then applied on the variable block sizes utilising the inter-correlation property of individual colour planes and interactions among the colour planes so as to reduce the vector dimension and hence the less computational cost. The proposed technique generates two dynamic codebooks and two backup codebooks adaptively and use rate-distortion cost criteria to encode both the homogeneous and complex regions separately. Since the proposed method encodes all the three colour components in single pass, the colour image encoding time is slightly higher than that of grey scale image coding but in contrast to three times in the existing colour image coding techniques.

关键词： backup codebooks grey scale image coding colour image coding techniques polynomials image colour analysis inter-correlation property computational cost input colour image Computer vision and image processing techniques integer programming image and video coding image coding Integral transforms complex regions variable block size adaptive vector quantisation low-level information Algebra colour image encoding time image classification mean-normalised variance classifier contrast edges integer-based orthogonal polynomials transform dynamic codebooks homogeneous regions rate distortion theory homogeneous redions transforms vector dimension rate-distortion cost criteria vector quantisation

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CSIS: Compressed sensing-based enhanced-embedding capacity image steganography scheme

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IET image PROCESSING 2021年第9期15卷 1909-1925页

作者： Agrawal, Rohit Ahuja, Kapil Indian Inst Technol Indore Math Data Sci MODS Lab Indore India

image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Hence, the goal here is to enhance the embedding capacity while preserving the visual quality of the stego-image. It is also intended to ensure that the scheme is resistant to steganalysis attacks. This paper proposes a compressed sensing image steganography (CSIS) scheme to achieve these goals. In CSIS, the cover image is sparsified block-wise, linear measurements are obtained, and then permissible measurements are selected. Next, the secret data is encrypted, and 2 bits of this encrypted data are embedded into each permissible measurement. For the reconstruction of the stego-image, ADMM and LASSO are used for the resultant optimization problem. Experiments are performed on several standard greyscale images and a colour image. Higher embedding capacity, 1.53 times more compared to the most recent scheme, is achieved. An average of 37.92 dB PSNR value, and average values close to 1 for both the mean SSIM index and the NCC coefficients are obtained, which is considered good. These metrics show that CSIS substantially outperforms existing similar steganography schemes.

关键词： Cryptography image and video coding Optimisation techniques Computer vision and image processing techniques Data security Optimisation techniques

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Visual sensitivity-based low-bit-rate image compression algorithm

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IET image PROCESSING 2012年第7期6卷 910-918页

作者： Xia, Q. Li, X. Zhuo, L. Lam, K. M. Beijing Univ Technol Signal & Informat Proc Lab Beijing Peoples R China Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Signal Proc Kowloon Hong Kong Peoples R China

In this study, the authors present a visual sensitivity-based low-bit-rate image compression algorithm. The authors algorithm combines both visual sensitivity and compression techniques so that a higher compression rate, with satisfactory visual quality, can be achieved. In the coding process, the input image is divided into blocks, and each block is classified as an edge block (EB), a textural block (TB) or a flat block (FB). For EBs, which are most important to the subjective quality of decoded images, the standard Joint Photographic Experts Group (JPEG) coding scheme with a tolerant quantisation step is employed so as to restrict the blocking artefacts caused by the quantisation error to an acceptable level. For FBs, a skipping scheme is employed on blocks in the compression process so as to save the bits. The coding of the skip blocks, identified by the skipping scheme, will make reference to the reconstructed regions of the image in the encoding process. Owing to the masking effects of the human visual system on high-frequency textures, standard JPEG compression coding with a greater quantisation step is employed on the down-scaled version of non-skip blocks and TBs. Experimental results show the superior performance of our method in terms of both compression efficiency and visual quality.

关键词： blocking artefacts edge block classification textural block classification visual sensitivity-based low-bit-rate image compression algorithm data compression image reconstruction Joint Photographic Experts Group Computer vision and image processing techniques image and video coding image coding standard JPEG compression coding skipping scheme quantisation (signal) visual quality image classification high-frequency textures image reconstruction regions image texture block codes skip block coding flat block classification quantisation error JPEG coding scheme

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Low-power context-based adaptive binary arithmetic encoder using an embedded cache

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IET image PROCESSING 2012年第4期6卷 309-317页

作者： Lei, S. -F. Lo, C. -C. Kuo, C. -C. Shieh, M. -D. Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan

H.264/AVC achieves a higher compression ratio than previous standards. However, this standard is also more complex because of the use of methods such as context-based adaptive binary arithmetic coding (CABAC). The high computational complexity of CABAC results in large power consumption. This study presents a systematic analysis for designing a low-power architecture which includes an embedded cache. The analysis provides the mapping scheme between the cache and the main memory where the contexts are stored. The observations for the proposed scheme are based on the statistical correlation between neighbouring blocks for H. 264 coding. The proposed scheme allows the context access operations to hit frequently in the cache, significantly reducing the power consumption. The proposed architecture lowers power consumption by up to 50% compared to designs without embedded cache. An efficient bit-packing method of output bitstream that can be implemented by pipeline structure for high encoding data throughput is also proposed. The throughput of the proposed design is up to 200 Mbins per second for H. 264 main profile.

关键词： Memory circuits CABAC embedded cache bit-packing method data compression H.264 coding image and video coding arithmetic codes low-power architecture Semiconductor storage H.264-AVC binary codes H.264 main profile statistical correlation video coding Computational complexity video signal processing computational complexity correlation theory cache storage high encoding data throughput low-power context-based adaptive binary arithmetic encoder pipeline structure power consumption main memory embedded systems Other topics in statistics higher compression ratio

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Use of optimal wavelet packet decomposition for the long-term prediction of variable-bit-rate video traffic

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IET COMMUNICATIONS 2010年第11期4卷 1277-1287页

作者： Zhang, S. Chen, J. She, L. -H. Bao, X. -R. Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

This study systematically investigates the long-term prediction of online, real-time variable-bit-rate (VBR) video traffic, which is a key and complicated component of advanced predictive dynamic bandwidth control for future networks such as multimedia satellite networks. As per the time variation, non-linearity and long-range dependence in the VBR video traffic trace, a novel method of feature extraction based on multi-scale decomposition is proposed. After an analysis of the time-frequency distribution characteristics of the video trace, the wavelet packets that have the trait of arbitrary distinction and decomposition are selected. After space partition of wavelet packets, the best wavelet packet basis for feature extraction is picked. Based on the best basis, fast arbitrary multi-scale wavelet packet transform (WPT) can be done and each higher dimension wavelet coefficient matrix can be obtained. Then, wavelet coefficients prediction is done based on normalised least mean square error (NLMS) or least mean-support vector machine (LS-SVM) algorithm. The long-term prediction of the VBR video traffic is obtained through reverse wavelet transforms on the predicted wavelet coefficients. Numerical and simulation results are provided to validate the claims.

关键词： image and video coding variable rate codes wavelet packet transform wavelet transforms dynamic bandwidth control bandwidth compression wavelet coefficient matrix least mean squares methods Codes multiscale decomposition arbitrary distinction video coding feature extraction VBR support vector machine video signal processing Mathematical analysis variable bit rate video traffic normalised least mean square error Integral transforms time-frequency analysis telecommunication traffic support vector machines

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Bi-level image compression technique using neural networks

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IET image PROCESSING 2012年第5期6卷 496-506页

作者： Sahami, S. Shayesteh, M. G. Urmia Univ Dept Elect Engn Orumiyeh Iran Sharif Univ Technol Wireless Res Lab ACRI Dept Elect Engn Tehran Iran

This study presents the utilisation of neural-network for bi-level image compression. In the proposed lossy compression method, the locations of pixels of image are applied to the inputs of a multilayer perceptron neural-network. The output of the network denotes the pixel intensity (0 or 1). The final weights of the trained neural-network are quantised, represented by a few bits, Huffman encoded and then stored as the compressed image. In the decompression phase, by applying the pixels locations to the trained network, the output determines the intensity. The results of experiments on more than 4000 different images indicate higher compression rate of the proposed structure compared with the commonly used methods such as Comite Consultatif International Telephonique of Telegraphique (CCITT) G4 and joint bi-level image experts group (JBIG2) standards. Moreover, quantisation issue in neural-network deployment is addressed and a solution is proposed. Further, an adaptive technique based on binary image characteristics is applied to achieve more compression rates.

关键词： binary image characteristics Neural computing techniques JBIG2 standards Huffman encoding data compression data compression rates multilayer perceptrons lossy compression method Computer vision and image processing techniques image and video coding image coding quantisation issue CCITT G4 quantisation (signal) trained neural-network joint bilevel image experts group standard adaptive technique multilayer perceptron neural-network bilevel image compression technique Comité Consultatif International Téléphonique of Télégraphique G4 Huffman codes decompression phase image pixel intensity

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On learning based compressed sensing for high resolution image reconstruction

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IET image PROCESSING 2021年第2期15卷 393-404页

作者： Islam, Sheikh Rafiul Maity, Santi P. Ray, Ajoy Kumar Indian Inst Engn Sci & Technol Dept Informat Technol Howrah 711103 W Bengal India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India

Compressed sensing (CS) or compressive sampling has shown an enormous potential to reconstruct a signal from its highly under-sampled observations. A high dimensional image processing system can adopt the CS paradigm to reduce the storage and the transmission burden. However, a large sensing system (matrix) is required to capture high dimensional images. The CS reconstruction algorithms demand heavy computational requirement due to the use of large sensing matrix. It sometimes becomes impractical to implement a sensing system of the desired size due to the limited access to the CPU memory. To address this issue, the present work proposes a deep learning based CS framework that uses a convolutional neural network to enable capturing of a high dimensional image by utilizing a multi-layer subsampling and filtering operations. The proposed approach uses another convolutional neural network that reconstructs the original image without depending on the sensing network at a significantly reduced computational cost. Extensive simulation results show that the proposed method reconstructs a high dimensional image with an improved PSNR value by 1.5 +/- 0.63 dB, SSIM value by 0.04 +/- 0.02 and FSIM value by 0.02 +/- 0.02 compared to the other state-of-the-art methods in less than 1.4 seconds.

关键词： image and video coding Computer vision and image processing techniques Neural nets

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IRES: An integrated software and hardware interface framework for reconfigurable embedded system

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IET COMPUTERS AND DIGITAL TECHNIQUES 2010年第1期4卷 27-37页

作者： Chiu, J. -C. Yeh, T. -L. Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 804 Taiwan

Hardware/software co-design is an interesting topic for most embedded system architects. However, designers find integrating hardware and software communications interface challenging. A framework for integrating the software and hardware communication interface for computing in reconfigurable embedded systems, called IRES, is proposed. The framework supports reconfigurable computing architectures, based on traditional central processing unit and the reconfigurable field programmable gate array, and composed of the integration linker, the boot loader, small task-oriented operating objects and the hardware management unit. The integration linker enables the IRES to link hardware net-list files and tasks into one execution. le, called the executor, constructed with the boot loader, the task-oriented operating kernel, the application tasks and the accelerating hardware functions. Task and hardware functions are segregated by program segment prefixes, designed to record interaction information of hardware and software resources. When the executor operates on the target-embedded environment, the implicit hardwire-call will be supported to invoke hardware functions in the task codes. The IRES successfully implements in the realised hardware platform, and this work verifies communication effectiveness between hardware and software through video compression applications.

关键词： central processing unit video coding boot loader data compression video signal processing Logic and switching circuits reconfigurable architectures integration linker image and video coding hardware management unit Data handling techniques Hardware-software codesign video compression applications Logic circuits field programmable gate arrays embedded system hardware-software codesign Computer architecture reconfigurable computing architectures embedded systems task-oriented operating objects reconfigurable field programmable gate array IRES system

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Depth map compression and depth-aided view rendering for a three-dimensional video system

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IET SIGNAL PROCESSING 2012年第3期6卷 247-254页

作者： Shao, F. Yu, M. Jiang, G. Li, F. Peng, Z. Ningbo Univ Fac Informat Sci & Engn Ningbo 315211 Zhejiang Peoples R China

Three-dimensional (3D) video technologies are becoming increasingly popular, as they can provide high quality and immersive experience to end users, where depth maps are employed to generate the virtual views by depth-image-based rendering technique. However, how to reduce the compression and rendering complexities for depth maps while maintaining high rendering quality is still unresolved. In this study, a novel depth map compression and depth-aided view rendering method is proposed. In the proposed method, depth maps are represented with different layers and compressed with different macroblock-mode decision procedure, and several optimisation techniques, including spatio-temporal consistent warping, colour correction and temporal consistent hole filling are embedded into the view rendering framework. Experimental results show that compared with the traditional method, the proposed method can reduce more than 79% compression computational complexity and more than 45% rendering computational complexity, while maintaining high rendering quality.

关键词： depth-image-based rendering technique data compression image and video coding rendering (computer graphics) compression complexities rendering computational complexity depth map compression three-dimensional video technologies video coding three-dimensional video system colour correction compression computational complexity rendering quality depth-aided view rendering video signal processing macroblock-mode decision procedure Computational complexity Graphics techniques computational complexity optimisation techniques temporal consistent hole filling rendering complexities spatio-temporal consistent warping

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