Pengyu Liu

A Novel Image Retrieval Algorithm Based on ROI by Using SIFT Feature Matching

This paper provides a novel content-based image retrieval algorithm based on ROI (Region Of Interest) by using SIFT (Scale Invariant Feature Transform) feature matching. SIFT descriptors, which are invariant to image scaling and transformation and rotation, and partially invariant to illumination changes and affine, present the local features of an image. Therefore, feature keypoints can be extracted more accurately by using SIFT from user-defined ROI of an image than color, texture, shape and spatial relations feature. To decrease unavailable features matching, a dynamic probability function replaces the original fixed value to determine the similarity distance of ROI and database from training images. These are the kernel of content-based image retrieval. The experimental results show that this method improves the stability and precision of image retrieval.

An Effective Image Retrieval Method Based on Color and Texture Combined Features

Image has various inherent features which reflect its content such as color, texture, shape, spatial relationship features etc. How to organize and utilize these features effectively and improve the retrieval performance is a valuable research topic. One of the key issues in image retrieval based on combined features is how to assign weight to different features. An image retrieval method combined color and texture features is proposed in this paper. According to image texture characteristic, a kind of image feature statistic is defined. By using feature weight assignment operators designed here, the method can assign weight to color and texture features according to image content adaptively and realize image retrieval based on combined image features. The experiment results show that the method mentioned above is more efficiently than those traditional image retrieval methods based on single visual feature or simple linear combined low- level visual features of fixed weight.

An Adaptive Motion Estimation Scheme for Video Coding

The unsymmetrical-cross multihexagon-grid search (UMHexagonS) is one of the best fast Motion Estimation (ME) algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV) distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised.

An Effective Web Content-Based Image Retrieval Algorithm by Using SIFT Feature

This paper provides an effective web content-based image retrieval algorithm by using SIFT (Scale Invariant Feature Transform) feature. Different from other existing text-based web image search engines, this algorithm can be applied to content-based web image search engine effectively. SIFT descriptors, which are invariant to image scaling and transformation and rotation, and partially invariant to illumination changes and affine, present the local features of an image. Therefore, feature keypoints saved as XML files can be extracted more accurately by using SIFT than by color, texture, shape and spatial relations feature. To decrease unavailable features matching, a dynamic probability function replaces the original fixed value to determine the similarity distance of ROI (Region of interest) and database from web training images. The experimental results show that this method improves the stability and precision of image retrieval.

A Layered Structure Prediction Method for Mode Decision in Video Encoding

Layered structure anticipation method for inter-frame prediction based on the spatial-temporal correlation and mode homology features of encoding block is proposed in this paper. Firstly, the non-equilibrium distribution of inter prediction is found, and a temporal-spatial related fast mode decision algorithm is proposed to realize a pre-determination of intra or inter mode in the P frame encoding. According to the analysis of multi-block-size of inter prediction and temporal-spatial continuity and consistency, design and implement multi-layer fast inter mode decision algorithm including skip mode pre-decision, macro-block division mode prediction and sub-block segmentation mode chosen. Simulation results show that the proposed algorithm can achieve reduction of 70.01% encoding time on average compared by exhaustive search algorithm for H.264/AVC. It obtains a significant reduction in the computational complexity without degradation in quality, which achieves robustness and comprehensive optimization of encoding performance.

A Fast and Novel Intra and Inter Modes Decision Prediction Algorithm for H.264/AVC Based on the Characteristics of Macro-block

H.264/AVC is a powerful and high performance video compression standard. To achieve high coding efficiency, it employs many new techniques, such as spatial prediction in intra coding, adaptive block size motion compensation, multiple reference pictures, and so on. Especially the complex intra and inter encoding modes, obtain notable coding gains. The high computation burden of mode decision procedure is a challenge to extensive application of H.264/AVC. In this paper, a novel decision algorithm for intra and inter modes is presented based on the characteristics of current macro-block. A fast adaptive intra and inter prediction algorithm is provided for further timesaving. By avoiding a large amount of prediction processing, the computation complexity can be greatly reduced. Experimental results indicated that the proposed algorithm compared by exhaustive search algorithm, can achieve reduction of 67.61% encoding time on average, with a negligible PSNR loss of only 0.03dB and a mere 0.86% bit rate increase.

Fast Coding Unit Encoding Mechanism for Low Complexity Video Coding

In high efficiency video coding (HEVC), coding tree contributes to excellent compression performance. However, coding tree brings extremely high computational complexity. Innovative works for improving coding tree to further reduce encoding time are stated in this paper. A novel low complexity coding tree mechanism is proposed for HEVC fast coding unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among CU distribution, quantization parameter (QP) and content change (CC). Secondly, a CU coding tree probability model is proposed for modeling and predicting CU distribution. Eventually, a CU coding tree probability update is proposed, aiming to address probabilistic model distortion problems caused by CC. Experimental results show that the proposed low complexity CU coding tree mechanism significantly reduces encoding time by 27% for lossy coding and 42% for visually lossless coding and lossless coding. The proposed low complexity CU coding tree mechanism devotes to improving coding performance under various application conditions.

A Self-Adaptive and Fast Motion Estimation Search Method for H.264/AVC

H.264/AVC is the outstanding and significant video compression standard developed by ITU-T/ISO/IEC Joint Video Team. Motion estimation (ME) plays a key role in H.264/AVC, it concerns greatly on computational complexity especially when using the full search (FS) algorithm. Although many fast ME algorithms have been proposed to reduce the huge calculation complexity instead of FS, the ME still can not satisfy the critical real-time application’s needs. In this paper, a fast integer pixel variable block ME algorithm based on JVT which accepted UMHexagonS algorithm is presented for H.264/AVC encoder. With special considerations on the motion activity of the current macro-block, several adaptive search strategies have been utilized to significantly improve the video coding performance. The simulation results shows that the proposed algorithm maintains an unnoticeable quality loss in terms of PSNR on average compared with FS and reduces nearly 20% ME time compared with UMHexagonS while maintaining coding efficiency.

A Novel Intra-frame Prediction Algorithm Based on Macro-block's Histogram for H.264/AVC

H.264/AVC is the newest video coding standard with high compression efficiency. Especially it adopts traveling algorithm to find the optimal mode for intra-frame prediction in spatial field, so it is quite complex and costs very long encoding-time. In this paper, the proposed fast mode decision scheme consists of two parts: early intra mode detection, which is to select intra 4 × 4 or intra 16 × 16 mode for each MB before encoding according to smoothness characteristic, and fast intra4 × 4 mode decision, which is to reduce choices of prediction directions from 9 to 3 by SAD of Mode 0 to Mode 2. Experimental results show that without the variety of PSNR basically, the compression time is 32.8% less than the original algorithm, and only increases 3.2% bits-rate on average. This algorithm improves the intra-frame coding efficiency, and is especially applicable for the high quality and complex sequence.

An Effective Motion Estimation Scheme for H.264/AVC

H.264/AVC is the outstanding and significant video compression standard developed by ITU-T/ISO/IEC Joint Video Team (JVT). Motion estimation (ME) plays a key role in H.264/AVC, it concerns greatly on computational complexity especially when using the full search (FS) algorithm. Although many fast ME algorithms have been proposed to reduce the huge calculation complexity instead of FS, the ME still can not satisfy the critical real-time applicationpsilas needs. In this paper, a fast integer pixel variable block motion estimation algorithm based on JVT which accepted UMHexagonS algorithm is presented for H.264/AVC encoder. With special considerations on the motion activity of the current macro-block, several techniques, i.e., adaptive search strategies have been utilized to significantly improve the video coding performance. The simulation results analysis shows that the proposed algorithm maintains an unnoticeable quality loss in terms of PSNR on average compared with FS and reduces nearly 20% motion estimation time compared with UMHexagonS while maintaining coding efficiency.