Quadtree Coding Research Articles

High-Capacity Reversible Data Hiding in Encrypted Images Based on Hierarchical Quad-Tree Coding and Multi-MSB Prediction

Nowadays, more and more researchers are interested in reversible data hiding in encrypted images (RDHEI), which can be applied in privacy protection and cloud storage. In this paper, a new RDHEI method on the basis of hierarchical quad-tree coding and multi-MSB (most significant bit) prediction is proposed. The content owner performs pixel prediction to obtain a prediction error image and explores the maximum embedding capacity of the prediction error image by hierarchical quad-tree coding before image encryption. According to the marked bits of vacated room capacity, the data hider can embed additional data into the room-vacated image without knowing the content of original image. Through the data hiding key and the encryption key, the legal receiver is able to conduct data extraction and image recovery separately. Experimental results show that the average embedding rates of the proposed method can separately reach 3.504 bpp (bits per pixel), 3.394 bpp, and 2.746 bpp on three well-known databases, BOSSBase, BOWS-2, and UCID, which are higher than some state-of-the-art methods.

Efficient Depth Intra Frame Coding in 3D-HEVC by Corner Points.

To improve the coding performance of depth maps, 3D-HEVC includes several new depth intra coding tools at the expense of increased complexity due to a flexible quadtree Coding Unit/Prediction Unit (CU/PU) partitioning structure and a huge number of intra mode candidates. Compared to natural images, depth maps contain large plain regions surrounded by sharp edges at the object boundaries. Our observation finds that the features proposed in the literature either speed up the CU/PU size decision or intra mode decision and they are also difficult to make proper predictions for CUs/PUs with the multi-directional edges in depth maps. In this work, we reveal that the CUs with multi-directional edges are highly correlated with the distribution of corner points (CPs) in the depth map. CP is proposed as a good feature that can guide to split the CUs with multi-directional edges into smaller units until only single directional edge remains. This smaller unit can then be well predicted by the conventional intra mode. Besides, a fast intra mode decision is also proposed for non-CP PUs, which prunes the conventional HEVC intra modes, skips the depth modeling mode decision, and early determines segment-wise depth coding. Furthermore, a two-step adaptive corner point selection technique is designed to make the proposed algorithm adaptive to frame content and quantization parameters, with the capability of providing the flexible tradeoff between the synthesized view quality and complexity. Simulation results show that the proposed algorithm can provide about 66% time reduction of the 3D-HEVC intra encoder without incurring noticeable performance degradation for synthesized views and it also outperforms the previous state-of-the-art algorithms in term of time reduction and ∆ BDBR.

Self-learning residual model for fast intra CU size decision in 3D-HEVC

As an extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC requires to encode multiple texture views and depth maps, which inherits the same quad-tree coding structure as HEVC. Due to the distinct properties of texture views and depth maps, existing fast intra prediction approaches were presented for the coding of texture views and depth maps, respectively. To further reduce the coding complexity of 3D-HEVC, a self-learning residual model-based fast coding unit (CU) size decision approach is proposed for the intra coding of both texture views and depth maps. Residual signal, which is defined as the difference between the original luminance pixel and the optimal prediction luminance pixel, is firstly extracted from each CU. Since residue signal is strongly correlated with the optimal CU partition, it is used as the feature of each CU. Then, a self-learning residual model is established by intra feature learning, which iteratively learns the features of the previously encoded coding tree unit (CTU) generated by itself. Finally, a binary classifier is developed with the self-learning residual model to early terminate CU size decision of both texture views and depth maps. Experimental results show the proposed fast intra CU size decision approach achieves 33.3% and 49.3% encoding time reduction on average for texture views and depth maps with negligible loss of overall video quality, respectively.

A New Rate-Complexity-Distortion Model for Fast Motion Estimation Algorithm in HEVC

In the high efficiency video coding (HEVC) standard, motion estimation (ME) adopts a quadtree coding structure and a larger search range to improve the coding performance. These advanced coding tools, however, dramatically increase the computational complexity. To accelerate ME, fast methods have been proposed that reduce ME complexity at the expense of rate-distortion (R-D) performance. However, none of these methods can claim their tradeoff to be optimal. In this paper, we propose an optimal fast motion estimation (FME) algorithm based on an analytical model of rate-complexity-distortion (R-C-D). We extend the traditional R-D model by introducing the ME complexity into it, which enables us to explicitly express the R-D performance under different complexity budgets. Based on the R-C-D model, the proposed FME finds the R-C-D optimized search ranges for some representative prediction units (PUs), which are then extended or refined dynamically according to motion characteristics for neighboring PUs. The proposed FME enables an R-D performance close to that of full search. When compared with the default FME method in the reference software of HEVC, the proposed fast algorithm can reduce the complexity by over $80\%$ while improving the R-D performance. Furthermore, our proposed FME algorithm is hardware friendly, as regular data flow enables high data reuse efficiency.

Image Super Resolution Using Fractal Coding and Residual Network

Fractal coding techniques are an effective tool for describing image textures. Considering the shortcomings of the existing image super‐resolution (SR) method, the large‐scale factor reconstruction performance is poor and the texture details are incomplete. In this paper, we propose an SR method based on error compensation and fractal coding. First, quadtree coding is performed on the image, and the similarity between the range block and the domain block is established to determine the fractal code. Then, through this similarity relationship, the attractor is reconstructed by super‐resolution fractal decoding to obtain an interpolated image. Finally, the fractal error of the fractal code is estimated by the depth residual network, and the estimated version of the error image is added as an error compensation term to the interpolation image to obtain the final reconstructed image. The network structure is jointly trained by a deep network and a shallow network. Residual learning is introduced to greatly improve the convergence speed and reconstruction accuracy of the network. Experiments with other state‐of‐the‐art methods on the benchmark datasets Set5, Set14, B100, and Urban100 show that our algorithm achieves competitive performance quantitatively and qualitatively, with subtle edges and vivid textures. Large‐scale factor images can also be reconstructed better.

Efficient CU and PU Decision Based on Neural Network and Gray Level Co-Occurrence Matrix for Intra Prediction of Screen Content Coding

Screen content coding (SCC) significantly improves the screen content compression efficiency over the high efficient video coding but at the cost of extremely huge computational complexity. The flexible quad-tree coding unit (CU) partitioning structure and the newly-introduced SCC intra modes are largely responsible for the high computational complexity. In order to meet the challenge of huge computational complexity of SCC, we propose an efficient CU and prediction unit (PU) decision based on neural network (NN) and gray level co-occurrence matrix (GLCM) for SCC intra prediction. The proposed efficient SCC intra prediction algorithm contains three stages, including NN-based CU classification model, efficient PU mode decision based on classification (EPMD), and efficient CU size decision based on GLCM and spatiotemporal correlations (ECSD). Consequently, the computational complexity of SCC intra prediction can be drastically reduced by replacing the brute-force search with EPMD and ECSD to decide the optimal combination of CU size and PU mode. In addition, an online updating method of weighted factor is introduced to cope with different characteristics of test sequences. In order to achieve a good tradeoff between complexity reduction and rate distortion (RD) performance, extensive experiments are conducted to select the optimal threshold for ECSD. The experimental results show that in comparison with the original SCC reference software, the proposed algorithm can reduce 49.33% intra coding time with 1.36% BDBR increase and 0.13 dB BDPSNR decrease. Meanwhile, the proposed algorithm outperforms eight state-of-the-art algorithms in terms of computational complexity reduction and RD performance.

Fast and effective CU size decision based on spatial and temporal homogeneity detection

High-Efficiency Video Coding (HEVC) is the latest video coding standard of the Joint Collaborative Team on Video Coding (JCT-VC). HEVC noticeably improves compression performance when compared with previous standards such as H264, and represents a major step forward in video compression technology. However, this improvement is achieved by increasing the complexity of the encoding process. HEVC employs a novel flexible quad-tree coding block partitioning structure that enables the use of large and multi-sized coding, prediction, and transform blocks. This system is more efficient but also more computationally demanding. In this article an optimized CU size decision algorithm is proposed to reduce the computational cost of quad-tree partitioning by means of spatial and temporal homogeneity analysis and classification, which are directly applied to the input image. If a CU is classified as spatially or temporally homogeneous the quad-tree recursive process is stopped. Furthermore, this image pre-analysis is performed using logic units and embedded hardware on a GPU, thus avoiding unnecessary waiting states, so the computational cost associated with this process is zero for the processor in charge of the encoding process. In comparison with the reference HM16.2 test model, the encoding time is reduced by up to 32.69%, with negligible quality loss and a maximum BD-Rate increase of 1.2% for low-delay P configuration.

Color Image Compression Based on Wavelet, Differential Pulse Code Modulation and Quadtree Coding

Color Image Compression Based on Wavelet, Differential Pulse Code Modulation and Quadtree Coding

Image Compression Based on Cubic Bezier Interpolation, Wavelet Transform, Polynomial Approximation, Quadtree Coding and High Order Shift Encoding

Image Compression Based on Cubic Bezier Interpolation, Wavelet Transform, Polynomial Approximation, Quadtree Coding and High Order Shift Encoding

Medical Ultrasound Video Coding with H.265/HEVC Based on ROI Extraction.

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems.

Novel distribution model of transformed coefficients in video coding using quad-tree structured block partitioning

Today’s video coding standard such as high efficiency video coding uses a full quad-tree structured block partitioning, so the underlying statistics of transformed coefficients becomes more complicated to estimate than the previous standards due to the coding structure. However, a statistical distribution of transformed residue is important for a design of a smart encoder. Thus, in this paper, we present a theoretic analysis of a distribution of transformed coefficients produced from an encoder using different transform sizes, and derive a probability density function (pdf) for the estimation. The proposed density model provides a more accurate distribution model than the conventional pdfs. Parameters are theoretically estimated, and rate-distortion model is established from the proposed pdf. We also apply the proposed method to a rate control problem to show the efficiency of the proposed density model. Our experimental results show that the proposed method is better capable of modeling the mixed sources of multiple-type transform coefficients occurred from the quad-tree coding structure of transform and provides an accurate estimate in rate control.

Hole-filling map-based coding unit size decision for dependent views in three-dimensional high-efficiency video coding

The three-dimensional high-efficiency video coding (3-D-HEVC) is an emerging compression standard for multiview video plus depth data. In addition to the quad-tree coding structure inherited from HEVC, some tools are integrated, which significantly improve the coding efficiency but also result in remarkably high computational complexity. We propose a fast coding unit (CU) size decision algorithm for both depth and texture components in dependent views, where hole-filling maps created through view synthesis are utilized. First, after coding the base view, warp it onto each dependent view via depth image based rendering, during which hole-filling maps are generated. Then for depth in dependent views, CU splitting can be early terminated considering the disoccluded information from hole-filling maps; for texture in dependent views, combining the disoccluded information and the interview correlations, the CU partitioning process can also be accelerated. Experimental results show that the proposed algorithm can achieve on average 54.3% time reduction, with a negligible Bjontegaard delta bitrate increase of 0.15% on synthesized views, and a 0.05% increase on all the coded plus synthesized views compared with the original encoding scheme in a 3-D-HEVC test model.

Machine learning based fast H.264/AVC to HEVC transcoding exploiting block partition similarity

Video transcoding is to convert one compressed video stream to another. In this paper, a fast H.264/AVC to High Efficiency Video Coding (HEVC) transcoding method based on machine learning is proposed by considering the similarity between compressed streams, especially the block partition correlations, to reduce the computational complexity. This becomes possible by constructing three-level binary classifiers to predict quad-tree Coding Unit (CU) partition in HEVC. Then, we propose a feature selection algorithm to get representative features to improve predication accuracy of the classification. In addition, we propose an adaptive probability threshold determination scheme to achieve a good trade-off between low coding complexity and high compression efficiency during the CU depth prediction in HEVC. Extensive experimental results demonstrate the proposed transcoder achieves complexity reduction of 50.2% and 49.2% on average under lowdelay P main and random access configurations while the rate-distortion degradation is negligible. The proposed scheme is proved more effective as comparing with the state-of-the-art benchmarks.

Color Image Compression Using DPCM with DCT, DWT and Quadtree Coding Scheme

This paper is concerned with the design and implementation of a compression method for color image. This method based on Differential Pulse Code Modulation (DPCM), Discrete Cosine Transform (DCT) , Discrete Wavelet Transform (DWT) and Quadtree Coding Scheme. As a first step the DPCM technique is used to isolate blocks of an image into correlated and uncorrelated blocks. The isolated correlated blocks have been compressed using DCT based compression method then each block has been embedded with zeros on the original image. Each uncorrelated block has been compressed using DWT based method and put the compressed block in its location on the original image. Then, the result (i.e., the zeros blocks and compressed blocks with DWT) coded using Quadtree spatial coding. The output from DWT based and DCT based passed through shift coding stage to gain a possible further compression. The performance results of proposed hybrid algorithms produces better quality of image in terms of Peak-Signal-to-Noise Ratio (PSNR) with a higher Compression Ratio (CR) compared to standalone DCT or DWT. The PSNR values of the reconstructed images after applying proposed system are ranged from 30.62 to 40.95dB and CR on average, have been reduced to be around 1:19.6 of the size of the original image.

Fast coding unit partitioning method based on edge detection for HEVC intra-coding

High efficiency video coding (HEVC) standard is the latest video coding standard generation. It employs powerful coding tools to obtain improved compression efficiency. To better exploit the redundancies, HEVC adopts a very flexible quad-tree coding structure, allowing the encoder to use a block partition that matches the image features. This exhaustive technique may achieve a higher coding efficiency; however, it induces a significant computational complexity in the encoding engine. This paper proposes a new texture parameter for classifying digital videos as a first contribution and then introduces an efficient coding unit (CU) partitioning algorithm based on the early defined texture parameter in order to speed up the encoding process. In fact, the proposed technique is based on edge detection by performing SOBEL filtering in order to decide the appropriate CU size. Compared to the original HEVC, the average execution time-saving is about 31 % while maintaining almost the same output video quality.

Fast Motion Estimation Algorithm and Design for Real Time QFHD High Efficiency Video Coding

Motion estimation (ME) in the latest High Efficiency Video Coding standard adopts the quadtree coding structure and up to a $64\times 64$ prediction unit (PU) size to improve the coding gain. However, these techniques also have serious design problems regarding the complexity, data dependency, external memory bandwidth, and on-chip buffer size compared with previous standards, especially for real-time ultrahigh-definition video coding. To solve these problems, this paper proposes an efficient ME design with a joint algorithm and architecture optimization. To reduce complexity, we propose a predictive integer ME (IME) algorithm that selects the most probable search directions and steps through a statistical analysis to reduce the number of search points by 90.5%. We also employ a PU size-dependent fractional ME (FME) algorithm to reduce the interpolation filtering by 62.4% compared with the reference software. To resolve the corresponding dependency, we cascade the IME and FME computations via interlaced scheduling and propose an early motion vector prediction candidate approach. We use this scheduling with a $16\times 16$ processing unit to compute the partial matching cost of all PUs with the same $16\times 16$ current block in an interlaced order and share their common reference block to reduce the on-chip buffer size and off-chip memory bandwidth. The bandwidth is further reduced by a cache with double Z scan indexed addressing to simplify the cache controller. Implementation with a Taiwan Semiconductor Manufacturing Company 90-nm CMOS process supports the real-time encoding of $4~{\rm K}\times 2~{\rm K}$ at 60 frames/s operated at 270 MHz with 778.7k logic gates and 17.4 KB of on-chip memory.

Fast HEVC Coding Unit Decision Based On BP-Neural Network

High Efficiency Video Coding (HEVC) employs a highly flexible quad-tree coding block partitioning structure characterized by a coding unit (CU), prediction unit (PU) and transform unit (TU). Taking the computational complexity of CU splitting in HEVC into consideration, a novel fast CU selection algorithm based on a back-propagation neural network (BPNN) is proposed in this paper. To find the best prediction mode at the current CU, the BPNN classifier is designed for two kinds of outputs representing “split” and “unsplit”. Thus, whether to split the current CU or not is directly judged by the outputs of the BPNN classifier. With this new scheme, the unnecessary CU splitting process can be skipped in advance. Experiments and results show that the proposed algorithm could greatly reduce the high computational complexity and the average time savings reaches 47.75% in Random Access Main configuration and 41.94% in Low Delay B Main configuration with a BD-bit rate increase of 1.89%.

A Fast TU Size Decision Method for HEVC RQT Coding

The emerging high efficiency video coding (HEVC) standard adopts the quadtree-structured transform unit (TU) in the residual quadtree (RQT) coding. Each TU allows to be split into four equal sub-TUs recursively. The RQT coding is performed for all the possible transform depth levels to achieve the highest coding efficiency, but it requires a very high computational complexity for HEVC encoders. In order to reduce the computational complexity requested by the RQT coding, in this paper, we propose a fast TU size decision method incorporating an adaptive maximum transform depth determination (AMTD) algorithm and a full check skipping - early termination (FCS-ET) algorithm. Because the optimal transform depth level is highly content-dependent, it is not necessary to perform the RQT coding at all transform depth levels. By the AMTD algorithm, the maximum transform depth level is determined for current treeblock to skip those transform depth levels rarely used by its spatially adjacent treeblocks. Additionally, the FCS-ET algorithm is introduced to exploit the correlations of transform depth level between four sub-CUs generated by one coding unit (CU) quadtree partitioning. Experimental results demonstrate that the proposed overall algorithm significantly reduces on average 21% computational complexity while maintaining almost the same rate distortion (RD) performance as the HEVC test model reference software, HM 13.0.

Fast intra mode decision algorithm for HEVC based on dominant edge assent distribution

As the latest video coding standard, high efficiency video coding (HEVC) is a successor to H.264/AVC. To improve the coding efficiency of intra coding, HEVC employs a flexible quad-tree coding block partitioning structure and 35 intra prediction modes. The optimal prediction mode is selected through rough mode decision (RMD) and rate distortion optimisation (RDO) process. Due to the huge search space of all of the possible depth levels (CU sizes) and intra prediction modes, intra coding of HEVC is a very time-consuming and complicated process, which limits the application of HEVC. In order to reduce the intra coding complexity, we propose a fast mode decision algorithm for HEVC intra prediction which is based on dominant edge assent (DEA) and its distribution. The four DEAs in the directions of degree 0, 45, 90 and 135 are computed first; then, the dominant edge is decided according to the minimum DEA. Next, a subset of prediction modes in accordance with the dominant edge is chosen for the RMD process. The rule is as follows: When the standard deviation of DEA is distinctly small, we skip the RMD process and take the direct current (DC) mode and planar modes as the candidate modes for the RDO process; when the minimum DEA is distinctly small, we select seven modes as the candidate modes for the RMD process; otherwise, we select 11 modes for the RMD process. Lastly, the prediction unit (PU) size-based number of RDO candidate modes (3 for PU size 4?×?4 and 8?×?8 and 1 for the other PU sizes) is modified according to experimental analysis. Compared with HM 9.1, Shen's proposal and da Silva's proposal, which are two state-of-the-art fast intra mode decision algorithms, the experimental results reveal that the proposed algorithm can save 36.26, 13.85 and 20.81 % coding time on average with a negligible loss of coding efficiency, respectively.

Initialization, Limitation, and Predictive Coding of the Depth and Texture Quadtree in 3D-HEVC

The 3D video extension of High Efficiency Video Coding (3D-HEVC) exploits texture-depth redundancies in 3D videos using intercomponent coding tools. It also inherits the same quadtree coding structure as HEVC for both components. The current software implementation of 3D-HEVC includes encoder shortcuts that speed up the quadtree construction process, but those are always accompanied by coding losses. Furthermore, since the texture and its associated depth represent the same scene, at the same time instant and view point, their quadtrees are closely linked. In this paper, an intercomponent tool is proposed in which this link is exploited to save both runtime and bits through a joint coding of the quadtrees. If depth is coded before the texture, the texture quadtree is initialized from the coded depth quadtree. Otherwise, the depth quadtree is limited to the coded texture quadtree. A 31% encoder runtime saving, a -0.3% gain for coded and synthesized views and a -1.8% gain for coded views are reported for the second method.