Liquan Shen

An Effective CU Size Decision Method for HEVC Encoders

The emerging high efficiency video coding standard (HEVC) adopts the quadtree-structured coding unit (CU). Each CU allows recursive splitting into four equal sub-CUs. At each depth level (CU size), the test model of HEVC (HM) performs motion estimation (ME) with different sizes including 2N × 2N, 2N × N, N × 2N and N × N. ME process in HM is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision algorithm for HM. Since the optimal depth level is highly content-dependent, it is not efficient to use all levels. We can determine CU depth range (including the minimum depth level and the maximum depth level) and skip some specific depth levels rarely used in the previous frame and neighboring CUs. Besides, the proposed algorithm also introduces early termination methods based on motion homogeneity checking, RD cost checking and SKIP mode checking to skip ME on unnecessary CU sizes. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity while maintaining almost the same RD performance as the original HEVC encoder.

Fast CU size decision and mode decision algorithm for HEVC intra coding

The emerging international standard of High Efficiency Video Coding (HEVC) is a successor to H.264/AVC. In the joint model of HEVC, the tree structured coding unit (CU) is adopted, which allows recursive splitting into four equally sized blocks. At each depth level, it enables up to 34 intra prediction modes. The intra mode decision process in HEVC is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision and mode decision algorithm for HEVC intra coding. Since the optimal CU depth level is highly content-dependent, it is not efficient to use a fixed CU depth range for a whole image. Therefore, we can skip some specific depth levels rarely used in spatially nearby CUs. Meanwhile, there are RD cost and prediction mode correlations among different depth levels or spatially nearby CUs. By fully exploiting these correlations, we can skip some prediction modes which are rarely used in the parent CUs in the upper depth levels or spatially nearby CUs. Experimental results demonstrate that the proposed algorithm can save 21% computational complexity on average with negligible loss of coding efficiency.

Effective CU size decision for HEVC intracoding.

In high efficiency video coding (HEVC), the tree structured coding unit (CU) is adopted to allow recursive splitting into four equally sized blocks. At each depth level (or CU size), it enables up to 35 intraprediction modes, including a planar mode, a dc mode, and 33 directional modes. The intraprediction via exhaustive mode search exploited in the test model of HEVC (HM) effectively improves coding efficiency, but results in a very high computational complexity. In this paper, a fast CU size decision algorithm for HEVC intracoding is proposed to speed up the process by reducing the number of candidate CU sizes required to be checked for each treeblock. The novelty of the proposed algorithm lies in the following two aspects: 1) an early determination of CU size decision with adaptive thresholds is developed based on the texture homogeneity and 2) a novel bypass strategy for intraprediction on large CU size is proposed based on the combination of texture property and coding information from neighboring coded CUs. Experimental results show that the proposed effective CU size decision algorithm achieves a computational complexity reduction up to 67%, while incurring only 0.06-dB loss on peak signal-to-noise ratio or 1.08% increase on bit rate compared with that of the original coding in HM.

Adaptive Inter-Mode Decision for HEVC Jointly Utilizing Inter-Level and Spatiotemporal Correlations

High Efficiency Video Coding (HEVC) adopts the quadtree structured coding unit (CU), which allows recursive splitting into four equally sized blocks. At each depth level, it enables SKIP mode, merge mode, inter 2N × 2N, inter 2N × N, inter N × 2N, inter 2N × nU, inter 2N × nD, inter nL x 2N, inter nR × 2N, inter N × N (only available for the smallest CU), intra 2N × 2N, and intra N × N (only available for the smallest CU) in inter-frames. Similar to H.264/AVC, the mode decision process in HEVC is performed using all the possible depth levels (or CU sizes) and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency, but leads to a very high computational complexity. Since the optimal prediction mode is highly content dependent, it is not efficient to use all the modes. In this paper, we propose a fast inter-mode decision algorithm for HEVC by jointly using the inter-level correlation of quadtree structure and the spatiotemporal correlation. There exist strong correlations of the prediction mode, the motion vector and RD cost between different depth levels and between spatially temporally adjacent CUs. We statistically analyze the prediction mode distribution at each depth level and the coding information correlation among the adjacent CUs. Based on the analysis results, three adaptive inter-mode decision strategies are proposed including early SKIP mode decision, prediction size correlation-based mode decision and RD cost correlation-based mode decision. Experimental results show that the proposed overall algorithm can save 49%-52% computational complexity on average with negligible loss of coding efficiency, exhibiting applicability to various types of video sequences.

Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut

In this paper, we propose an unsupervised salient object segmentation approach based on kernel density estimation (KDE) and two-phase graph cut. A set of KDE models are first constructed based on the pre-segmentation result of the input image, and then for each pixel, a set of likelihoods to fit all KDE models are calculated accordingly. The color saliency and spatial saliency of each KDE model are then evaluated based on its color distinctiveness and spatial distribution, and the pixel-wise saliency map is generated by integrating likelihood measures of pixels and saliency measures of KDE models. In the first phase of salient object segmentation, the saliency map based graph cut is exploited to obtain an initial segmentation result. In the second phase, the segmentation is further refined based on an iterative seed adjustment method, which efficiently utilizes the information of minimum cut generated using the KDE model based graph cut, and exploits a balancing weight update scheme for convergence of segmentation refinement. Experimental results on a dataset containing 1000 test images with ground truths demonstrate the better segmentation performance of our approach.

View-Adaptive Motion Estimation and Disparity Estimation for Low Complexity Multiview Video Coding

The emerging international standard for multiview video coding (MVC) is an extension of H.264/advanced video coding. In the joint mode of MVC, both motion estimation (ME) and disparity estimation (DE) are included in the encoding process. This achieves the highest coding efficiency but requires a very high computational complexity. In this letter, we propose a fast ME and DE algorithm that adaptively utilizes the inter-view correlation. The coding mode complexity and the motion homogeneity of a macroblock (MB) are first analyzed according to the coding modes and motion vectors from the corresponding MBs in the neighbor views, which are located by means of global disparity vector. According to the coding mode complexity and the motion homogeneity, the proposed algorithm adjusts the search strategies for different types of MBs in order to perform a precise search according to video content. Experimental results demonstrate that the proposed algorithm can save 85% computational complexity on average, with negligible loss of coding efficiency.

Selective Disparity Estimation and Variable Size Motion Estimation Based on Motion Homogeneity for Multi-View Coding

Multi-view video coding (MVC) is an ongoing standard in which variable size disparity estimation (DE) and motion estimation (ME) are both employed to select the best coding mode for each macroblock (MB). This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs it from practical use. In this paper, a fast DE and ME algorithm based on motion homogeneity is proposed to reduce MVC computational complexity. The basic idea of the method is to utilize the spatial property of motion field in prediction where DE and variable size ME are needed, and only in these regions DE and variable size ME are enabled. The motion field is generated by the corresponding motion vectors (MVs) in spatial window. Simulation results show that the proposed algorithm can save 63% average computational complexity, with negligible loss of coding efficiency.

Fast Inter Mode Decision Using Spatial Property of Motion Field

Variable size motion estimation with multiple reference frames has been adopted by the new video coding standard H.264. It can achieve significant coding efficiency compared to coding a macroblock (MB) in regular size with single reference frame. On the other hand, it causes high computational complexity of motion estimation at the encoder. Rate distortion optimized (RDO) decision is one powerful method to choose the best coding mode among all combinations of block sizes and reference frames, but it requires extremely high computation. In this paper, a fast inter mode decision is proposed to decide best prediction mode utilizing the spatial continuity of motion field, which is generated by motion vectors from 4times4 motion estimation. Motion continuity of each MB is decided based on the motion edge map detected by the Sobel operator. Based on the motion continuity of a MB, only a small number of block sizes are selected in motion estimation and RDO computation process. Simulation results show that our algorithm can save more than 50% computational complexity, with negligible loss of coding efficiency.

An Efficient Intermode Decision Algorithm Based on Motion Homogeneity for H.264/AVC

The latest video coding standard H.264/AVC significantly outperforms previous standards in terms of coding efficiency. H.264/AVC adopts variable block sizes ranging from 4 times 4 to 16 times 16 in inter frame coding, and achieves significant gain in coding efficiency compared to coding a macroblock (MB) using regular block size. However, this new feature causes extremely high computation complexity when rate-distortion optimization (RDO) is performed using the scheme of full mode decision. This paper presents an efficient intermode decision algorithm based on motion homogeneity evaluated on a normalized motion vector (MV) field, which is generated using MVs from motion estimation on the block size of 4 times 4. Three directional motion homogeneity measures derived from the normalized MV field are exploited to determine a subset of candidate intermodes for each MB, and unnecessary RDO calculations on other intermodes can be skipped. Experimental results demonstrate that our algorithm can reduce the entire encoding time about 40% on average, without any noticeable loss of coding efficiency.

Co-Saliency Detection Based on Hierarchical Segmentation

Co-saliency detection, an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of images. This letter proposes a hierarchical segmentation based co-saliency model. On the basis of fine segmentation, regional histograms are used to measure regional similarities between region pairs in the image set, and regional contrasts within each image are exploited to evaluate the intra-saliency of each region. On the basis of coarse segmentation, an object prior for each region is measured based on the connectivity with image borders. Finally, the global similarity of each region is derived based on regional similarity measures, and then effectively integrated with intra-saliency map and object prior map to generate the co-saliency map for each image. Experimental results on two benchmark datasets demonstrate the better co-saliency detection performance of the proposed model compared to the state-of-the-art co-saliency models.