High efficiency video coding (HEVC) achieves higher coding efficiency than previous standards but introduces a large computational complexity. Because HEVC adopted some new advanced tools and the most prominent one is the flexible hierarchical coding structures which include coding unit (CU), prediction unit (PU), transform unit (TU). All of those units must be test through rate-distortion optimisation. Since the CU is highly content dependent it is not efficient to test all the modes. In this study, the authors propose a fast coding algorithm for HEVC based on video contents. The authors statistically analysis the features of video contents from three aspects, the pixel gradient, the block mean value, and the block variance of CU. Then, jointly use these features with the CU depth levels and the prediction modes of spatiotemporal adjacent CUs to realise fast CU depth level decision and fast prediction mode decision. The experimental results show that the proposed algorithm can save 58.9 and 57.6% computational complexity on average with only average 1.8 and 1.9% bitrate losses under ‘random access, main’ and ‘low-delay, main’ conditions, respectively.

In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (∼1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.

This study presents a multi-level 2D discrete wavelet transform (DWT) architecture without off-chip RAM. Existing architectures use one off-chip RAM to store the image data, which increases the complexity of the system. For one-chip design, line-based architecture based on modified lifting scheme is proposed. By replacing the multipliers with canonic sign digit multipliers, a critical path of one full-adder delay is achieved. As per theoretical estimate, for three-level 2D DWT with an image of N × N size, the proposed architecture requires 123 adders, 66 subtracters, 167 registers, temporal memory of 7.5N words and input RAM of 3N bytes. The estimated hardware requirement shows that for the image size of 512 × 512 and three-level DWT, the proposed architecture involves at least 14.1% less transistor-delay-product than existing architectures.

Recently, low-rank representation (LRR)-based techniques have manifested remarkable results for data representation. To exploit the latent manifold structure of data, the graph regulariser is incorporated into the model of LRR. However, it is critical to construct an appropriate graph model and set the corresponding parameters. In addition, this procedure is usually time-consuming and proved to be overfitting when using cross validation or discrete grid search. Two novel LRR-based methods, called multiple graph regularised LRR and multiple hypergraph regularised LLR, are proposed to represent the high-dimensional data. To guarantee the smoothness along the estimated manifold, the multiple graph regulariser and the multiple hypergraph regulariser are incorporated into the traditional LRR method, respectively, which results in a unified framework. Moreover, the augmented Lagrange multiplier is adopted to solve the proposed models. Extensive experiments on real image datasets show the effectiveness of the proposed methods.

In high definition (HD) video coders, huge memory access bandwidth is the major throughput bottleneck. Lossless embedded compression is an efficient solution to alleviate the bandwidth burden, in which image are compressed before writing into local memory and decompressed after retrieving from local memory. This study proposes a hardware-oriented lossless image compression algorithm, supporting block and line random access flexibly for adapting diverse hardware video codec architectures. The major contributions are characterised as follows. First, block or pixel-level adaptive prediction is proposed to fully utilise the image spatial correlation by employing adaptive mode decision. Second, multiple-range semi-fixed (SF) variable length coding (VLC) is employed to describe the prediction residue, and adaptive block size selection is employed for SF VLC to fully utilise the statistical redundancy. In addition, Huffman VLC is further employed to represent the control syntax elements. Third, four-stage pipeline hardware architecture is proposed to implement the proposed algorithm. Simulation results show that the proposed algorithm achieves competitive rate compression performance compared with reference algorithms. The proposed hardware architecture is verified supporting real-time processing for quad-HD videos at the frequency of 166 MHz. The proposed work achieves reducing memory access bandwidth by ∼55.2%, which is useful for hardwired video coding.

In radiotherapy using 18-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET), the accurate delineation of the biological tumour volume (BTV) is a crucial step. In this study, the authors suggest a new approach to segment the BTV in ¹⁸F-FDG-PET images. The technique is based on the k-means clustering algorithm incorporating automatic optimal cluster number estimation, using intrinsic positron emission tomography image information. Clinical dataset of seven patients have a laryngeal tumour with the actual BTV defined by histology serves as a reference, were included in this study for the evaluation of results. Promising results obtained by the proposed approach with a mean error equal to (0.7%) compared with other existing methods in clinical routine, including fuzzy c-means with (35.58%), gradient-based method with (19.14%) and threshold-based methods.

Detection of small targets in an infrared (IR) image with high reliability is very important for defence systems. Small targets in an IR image are defined as salient features which attract the attention of human visual system. In this study, a robust method for detection of small targets in an IR image is proposed based on HV attention. In this method, first, the Gaussian-like feature maps are extracted from the original image. Then, saliency maps (SMs) are created based on pulsed discrete cosine transform, in which the target is salient and background clutter is suppressed. Finally, to increase the contrast between target and background clutter and to raise robustness of this method against false alarms, SMs are fused adaptively. Experiments are carried out on the data set including real-life IR images with small targets as well as various and complicated backgrounds. Qualitative and quantitative assessments show that the proposed method can detect small targets in IR image with high reliability and is more effective compared with other methods based on HV attention. Therefore, it can be used in many applications for detection of small targets in IR image with minimum false alarms.

Firefly algorithm (FA) is one of the newly developed nature inspired optimisation algorithm, inspired by the flashing behaviour of fireflies that a firefly tends to be attracted towards other fireflies with higher brightness. Thus FA has two advantages: local attractions and automatic regrouping. Based on these good properties, a novel image watermarking method based on FA in discrete wavelet transform (DWT)-QR transform domain is proposed in this study. Structural similarity index measure and bit error rate are used in the objective function to trade-off invisibility and robustness. The experiment results show that the proposed image watermarking method not only meet the need of invisibility, but also has better or comparable robustness as compared with some related methods.

This study proposes a line segment detection that can efficiently and effectively handle non-linear uniform intensity changes. The presented sketching algorithm applies the resistant to affine transformation and monotonic intensity change (RATMIC) descriptor to conduct binary translation in the image pre-processing step, which can remove the unwanted smoothing of the Canny detector in most line detections. The Harris corner detector is applied to catch regions of line segments for the purpose of simulating the composition of sketching and achieving a sense of unity within the picture. Furthermore, the RATMIC descriptor is employed to obtain binary images of the regions of interest (ROIs). Finally, small eigenvalue analysis is implemented to detect straight lines in the ROIs. The experiments conducted on various images with image rotation, scaling, and translation validate the effectiveness of the proposed method. The experimental results also demonstrate that about 30% in the overall coverage of major lines and 20% in the coverage per major line are increased compared with the state-of-the-art line detectors. Moreover, the performance of the proposed method produces a combined advantage of ∼17% in the coverage of line segments over the line segment detector with noisy images.

Speckle suppression is extremely important for understanding and utilising synthetic aperture radar (SAR) images, while the emphasis of the traditional methods for speckle suppression is usually focused on removing the noise instead of keeping the scattering character of imaging objects, which has caused serious interference to the subsequent applications of SAR images. The authors aim to develop sparse partial differential equation (PDE) for speckle suppression of SAR image, where the PDE model is associated with the sparse prior of objects and the statistical property of speckle. The PDE model has been proved to have the ability of denoising and edge-preserving by proper design, and the sparse prior of the point- and line-like objects on SAR images has been also illustrated, both of which help for keeping the scattering characters. To solve the proposed sparse PDE model, a numerical algorithm is designed and the sparse constraint is realised in each step of the diffusion process. In experiments, several real SAR images are utilised to validate the performance of the proposed method.

With the advancement in computer vision and graphics tools, digital compositing has become an integral part of the present computer-generated visual effects. However, factors such as inaccurate mask generation, intensive user interaction, and the creation of boundary seams due to colour or texture difference, make it hard to achieve quality composites. Poisson editing efficiently generates seamless composites but results in undesirable colour bleeding. Multi-resolution blending, in contrast, produces colour consistent composites; however often introduces blurry boundaries around the source object inserted. Motivated by these observations, the authors propose a colour consistent seamless compositing pipeline by integrating two new approaches. First, the authors use a visual attention algorithm based on the colour difference with increased edge weight using Gaussian filter bank (GFB) to ensure the least user interaction during mask generation. Second, the authors propose a hybrid framework by incorporating the goodness of the two different blending methods namely modified Poisson editing (MPE) and GFB-based multi-resolution blending to create colour consistent seamless composites. An extensive experiment has been carried out on challenging datasets to validate the proposed technique. Comparison with the state-of-the-art techniques shows the efficacy of the authors’ algorithm in generating colour consistent, seamless, and natural-looking composites for present image editing applications.

A novel general-purpose no-reference image quality assessment (NR-IQA) model utilising hybrid natural scene statistics (HNSS) is proposed. Distinguished from existing NR-IQA approaches, the new model combines the statistics of locally mean subtracted and contrast normalised coefficients in the spatial domain and the statistics of image patch coefficients in a codebook space, which is constructed by codebooks extracted from pristine images using K-Means. The authors demonstrate that the coefficients in the codebook space keep the NSS characteristics as same as these in the spatial domain. After extracting the statistical features, a two-stage framework of distortion classification followed by quality assessment is applied. Experimental results show that the authors’ predicted quality score well matches human perceptual image quality. The proposed model outperforms state-of-the-art general-purpose NR-IQA approaches when it is tested on the LIVE database.