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Image Processing, IET

Issue 4 • Date June 2012

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Displaying Results 1 - 15 of 15
  • Design of a context-adaptive variable length encoder for real-time video compression on reconfigurable platforms

    Publication Year: 2012 , Page(s): 301 - 308
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (516 KB)  

    In this study, a new context-adaptive variable length-coding encoder architecture is proposed particularly aimed to be implemented with field programmable logics (FPL) like FPGAs. The design implements different approaches in order to minimise the area cost as well as to speed up the coding efficiency, which allows real-time compression of 1080 p video streams coded in YCbCr 4:2:0 format. Priority cascading logics have been implemented in order to increase the parallelisation degree of the pre-coding stage, thus favouring the limitation of the number of clock cycles needed for the extraction of symbols from the input data, whereas the employment of the arithmetic table elimination technique has allowed a large-area reduction of the encoder thanks to the elimination of 18 of the 38 tables needed for the encoding stage. The design achieves real time elaboration with an operation frequency of 63 MHz and occupies 2200 look-up table (LUT)s when implemented on a low-cost, low-end XILINX Spartan 3 FPGA, thus overcoming the most recent FPL implementation and making this encoder quite comparable both in terms of area and speed with some recently proposed ASIC implementations, so that it turns out to be a valid alternative also for application specific implementations. View full abstract»

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

    Publication Year: 2012 , Page(s): 309 - 317
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (640 KB)  

    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. View full abstract»

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  • Vector quantisation index compression based on a coding tree assignment scheme with improved search-order coding algorithms

    Publication Year: 2012 , Page(s): 318 - 326
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (514 KB)  

    This study proposes a Coding Tree Assignment Scheme with Improved Search-Order Coding algorithms (CTAS-ISOC) to enhance the coding efficiency of the original SOC by exploiting the correlations of the neighbouring blocks using the left-pair and upper-pair patterns in the index domain. The essential techniques consist of three major elements: the Neighbouring Index Code Assignment (NICA), the Left-pair Search-Order Coding (LSOC) and the Upper-pair Search-Order Coding (USOC). The NICA approach assigns a short code to the current index by using the statistics on the indices of the neighbouring blocks. The LSOC (USOC) compares the current left (upper) index pair with previous index pairs in a predefined search path. Since the predefined search path is exploited with a correlation viewpoint, both LSOC and USOC achieve better compression than the original SOC. Experimental results demonstrate the effectiveness of the proposed scheme in comparison with some existing popular lossless index coding schemes. View full abstract»

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  • Shape retrieval based on manifold learning by fusion of dissimilarity measures

    Publication Year: 2012 , Page(s): 327 - 336
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (845 KB)  

    Content-based image retrieval (CBIR) is one of the most important research areas with applications in digital libraries, multimedia databases and the internet. Colour, texture, shape and spatial relations between objects are major features used in retrieval. Shape features are powerful clues for object identification. In this study, for improving retrieval accuracy, dissimilarities of contour and region-based shape retrieval methods were used. It is assumed that the fusion of two categories of shape description causes a considerable improvement in retrieval performance. The main goal in this study is to propose a new feature vector to coincide semantic and Euclidean distances. To accomplish this, the desired topological manifold was learnt by a distance-driven non-linear feature extraction method. The experiments showed that the geometrical distances between the samples on the manifold space are more related to their semantic distance. The proposed method was compared with other well-known approaches by MPEG-7 part B and Fish shape data sets. The results confirmed the effectiveness and validity of the proposed method. View full abstract»

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  • Learning-based super-resolution method with a combining of both global and local constraints

    Publication Year: 2012 , Page(s): 337 - 344
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (660 KB)  

    Learning-based super-resolution (SR) methods are popular in many applications recently. In these methods, the high-frequency details are usually found or combined through patch matching from training database. However, the representation ability of small patch is limited and it is difficult to guarantee that the super-resolved image is the best under the global view. To this end, the authors propose a statistical learning method for SR with both global and local constraints. More specifically, they introduce a mixture model into maximum a posteriori (MAP) estimation, which combines a global parametric constraint with a patch-based local non-parametric constraint. The global parametric constraint guarantees the super-resolved global image to agree with the sparse property of natural images, and the local non-parametric constraint is used to infer the residues between the image derived from the global constraint and the ground truth high-resolution (HR) image. Compared with the traditional patch-based learning methods without the global constraint, our method can not only preserve global image structure, but also restore the local details more effectively. Experiments verify the effectiveness of the proposed method. View full abstract»

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  • Unified framework for colourising corrupted image

    Publication Year: 2012 , Page(s): 345 - 353
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1258 KB)  

    Colourisation is a process of adding colours to greyscale image. It is noticed that the colourisation process for corrupted image usually needs image to be completed and then colourised. In this study the authors present a novel scribble-based colourisation algorithm of corrupted greyscale image. The authors construct a block-based bilateral filter (BBF) framework for image completion and colourisation. Distance transform and an adaptive weight selection scheme are introduced to the BBF framework in order to achieve improvements of accuracy and speed. The rationale behind the authors' algorithm is that image completion and colourisation can be both regarded as missing data recovery problem. Unlike existing sequential image completion method and colourisation method, the authors'algorithm can accomplish image completion and colourisation work under a proposed novel unified framework. Experiments results show the benefits of the authors'method. View full abstract»

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  • Robust image watermarking via geometrically invariant feature points and image normalisation

    Publication Year: 2012 , Page(s): 354 - 363
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (480 KB)  

    The robustness of watermarks to geometric attacks is viewed as an issue of great importance. Indeed, it constitutes one of the most challenging design requirements for watermarks. This study proposes a robust image watermarking scheme using visually significant feature points and image normalisation. In order to tackle the issue of geometric distortions, the authors adopt a feature extraction method based on end-stopped wavelets to extract significant geometry preserving feature points, which are shown to be robust against various types of common signal processing and geometric attacks. These feature points can be used as synchronisation marks between watermark embedding and detection. The watermark is embedded into non-overlapping normalised circular images, which are determined by feature points. Rotation invariance is achieved via image normalisation. The watermark embedding process is performed by modifying low-frequency coefficients of discrete cosine transform blocks, which are randomly selected using a secret key. Moreover, the security of the scheme is further guaranteed by an image-dependent key. The proposed scheme is blind as the original image is not required at the watermark detection. Experimental results show that the proposed scheme is robust against geometric attacks as well as common signal processing attacks and outperforms related techniques found in the literature. View full abstract»

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  • Wavelet-based oblivious image watermarking scheme using genetic algorithm

    Publication Year: 2012 , Page(s): 364 - 373
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (573 KB)  

    In this work, a robust and oblivious image watermarking scheme based on discrete wavelet transform (DWT) for copyright protection is presented. The original unmarked image is not required for watermark extraction. Third-level DWT is applied to the original cover image. Third- and second-level horizontal detail sub-band (LH2 and LH3) coefficients are grouped into different blocks. Grouping of the coefficients is done in such a way that each block should contain one coefficient from LH3 sub-band and four coefficients from LH2 sub-band. In each block, the first minimum and the second minimum are identified and modified according to the watermark bit. After watermark insertion, inverse DWT is applied to the sub-bands with modified coefficients to obtain the watermarked image. For watermark extraction, a threshold-based decoder is designed. Embedding and extraction process are characterised with parameters and genetic algorithm is used for parameter optimisation. Optimisation is to maximise the values of peak signal-to-noise ratio of the watermarked image and normalised cross correlation of the extracted watermark. The performance of the proposed scheme is compared with the existing schemes and significant improvement is observed. Experimental results show that, this algorithm is highly robust for many image attacks on the watermarked image. View full abstract»

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  • Ripplet transform type II transform for feature extraction

    Publication Year: 2012 , Page(s): 374 - 385
    Cited by:  Papers (2)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1123 KB)  

    Current image representation schemes have limited capability of representing two-dimensional (2D) singularities (e.g. edges in an image). Wavelet transform has better performance in representing one-dimensional (1D) singularities than Fourier transform. Recently invented ridgelet and curvelet transform achieve better performance in resolving 2D singularities than wavelet transform. To further improve the capability of representing 2D singularities, this study proposes a new transform called ripplet transform type II (ripplet-II). The new transform is able to capture 2D singularities along a family of curves in images. In fact, ridgelet transform is a special case of ripplet-II transform with degree 1. Ripplet-II transform provides the freedom in parameter settings, which can be optimised for specific problems. Ripplet-II transform can be used for feature extraction because of its efficiency in representing edges and textures. Experiments in texture classification and image retrieval demonstrate that the ripplet-II transform-based scheme outperforms wavelet and ridgelet transform-based approaches. View full abstract»

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  • Robust watermarking using fractional wavelet packet transform

    Publication Year: 2012 , Page(s): 386 - 397
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1169 KB)  

    In this study, the concept of fractional wavelet packet transform is explored with its application in digital watermarking. The core idea of the proposed watermarking scheme is to decompose an image via fractional wavelet packet transform and then a reference image is created by changing the positions of all frequency sub-bands at each level with respect to some rule which is secret and only known to the owner/creator. For embedding, the reference image is segmented into non-overlapping blocks and modify its singular values with the watermark singular values. Finally, a reliable watermark extraction algorithm is developed for the extraction of watermark from the distorted image. The feasibility of this method and its robustness against different kind of attacks are verified by computer simulations. View full abstract»

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  • Vector-field-based deformations for three-dimensional texture synthesis

    Publication Year: 2012 , Page(s): 398 - 406
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1236 KB)  

    A major drawback of conventional approaches to three-dimensional (3D) texture synthesis is the lack of fine-scale deformations in the synthesised results. This study presents a novel vector-field-based deformation approach to synthesising 3D textures with fine-scale deformations. The pre-process of this approach constructs feature vectors and similarity sets of voxels from an input 3D exemplar to accelerate neighbourhood matching. The synthesis process first introduces 3D vector fields for deforming synthesised results. A 3D pyramid synthesis technique integrated with 3D vector fields is then used to synthesise 3D textures. The proposed approach uses only eight neighbourhoods of each voxel for neighbourhood matching. Experimental results show that the proposed approach efficiently synthesises 3D textures with fine-scale deformations. View full abstract»

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  • Matching parameter estimation by using the radial basis function interpolation

    Publication Year: 2012 , Page(s): 407 - 416
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (319 KB)  

    A matching parameter estimation method with subpixel accuracy is derived by using the radial basis function (RBF) interpolation. This method reconstructs two analogue images from two given digital images by the RBF, and then minimises a non-linear cost function by the steepest-descent algorithm to estimate translation, rotation, scaling factor and intensity change between the two analogue images. The RBF provides accurate interpolation, resulting in accurate estimation. A Gaussian weighting function is introduced into the cost function to provide a local estimate within a region of interest (ROC). Then double integrals included in the cost function are analytically computed and the computational complexity is significantly reduced by exploiting the property that the Gaussian function decays rapidly. When the matching parameters are not constant over the whole image, or equivalently, the ROC is set to be small, the proposed method is better than the conventional phase correlation method in estimation accuracy. View full abstract»

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  • Practical photoquantity measurement using a camera

    Publication Year: 2012 , Page(s): 417 - 425
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (498 KB)  

    An image output by a camera is generally not a faithful representation of the real scene, because it undergoes a series of radiometric disturbances during the imaging process. This study proposes a method for obtaining a more accurate measure of the light seen by a camera. The proposed method requires no specific calibration apparatus and only minimal supervision. Nevertheless, it is quite comprehensive, since it accounts for response function, exposure, vignetting, spatial non-uniformity of the sensor and colour balancing. This method works in two steps. First, the camera is calibrated off-line, in a photoquantity sense. Then, the photoquantity of any scene can be estimated in-line.The method is therefore geared to a wide range of computer vision applications where a camera is expected to give a measurement of the visible light. This study starts by presenting a photoquantity model of the camera-imaging process. It then describes the key steps of calibration and correction method. Finally, the results are given and analysed to evaluate the relevance of this approach. View full abstract»

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  • Modified two-dimensional otsu image segmentation algorithm and fast realisation

    Publication Year: 2012 , Page(s): 426 - 433
    Cited by:  Papers (6)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1005 KB)  

    Traditional two-dimensional (2D) Otsu method supposes that the sum of probabilities of diagonal quadrants in 2D histogram is approximately one. This studies experiments and theory prove that the sum of probabilities of off-diagonal quadrants in 2D histogram is not always very small and this could not be neglected. Therefore the assumption mentioned above in 2D Otsu method is inadequately reasonable. In this study, an improved 2D Otsu segmentation method and recursive algorithm are proposed. By calculating probabilities of diagonal quadrants in 2D histogram separately, modified method is acquired. Experimental results show that proposed method can obtain better performance of segmentation than the traditional 2D Otsu method. The computation complexity of improved 2D Otsu method is equal to traditional 2D Otsu method. View full abstract»

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  • Multiple weighted prediction models for video coding with brightness variations

    Publication Year: 2012 , Page(s): 434 - 443
    Cited by:  Papers (1)
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (474 KB)  

    Weighted prediction (WP) is a tool introduced in H.264 to improve motion-compensation performance for video sequences with brightness variations. Various WP models to estimate the parameter set have been discussed in the literature. However, no single WP model works well for all types of brightness variations. A single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in a single reference frame. The proposed scheme makes a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. It enables different macroblocks in the same frame to use different WP models even when they are predicted from the same reference frame. Furthermore, a new re-ordering mechanism for SRefMWP is also proposed to guarantee that the list of the reference picture is in the best order for further decreasing the bit rate. To reduce the implementation cost, a reduction in the memory requirement is achieved via look-up tables (LUTs). Experimental results show that SRefMWP can achieve significant coding gain in scenes with different types of brightness variations. Furthermore, SRefMWP with LUTs can reduce the memory requirement by about 80% while keeping the same coding efficiency as SRefMWP. View full abstract»

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Aims & Scope

The range of topics covered by IET Image Processing includes areas related to the generation, processing and communication of visual information.

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