Ruobing Zou

A novel fast two step sub-pixel motion estimation algorithm in HEVC

Motion estimation (ME) is one of the most time consuming parts in video coding standard. As fast integer-pixel ME algorithm becoming more and more powerful, it is important to develop fast sub-pixel ME algorithm since the computational complexity of sub-pixel ME compared to integer-pixel ME has become relatively significant. In this paper, a novel fast sub-pixel ME algorithm is proposed. This algorithm first approximates the error surface of the sub-pixel position by a second order function and predicts the minimum point by minimizing the function at half-pixel accuracy. Then another second order approximation within a smaller area which is determined by the previous step is modeled to predict the best sub-pixel position. Experimental results show that the proposed method can reduce the sub-pixel search points significantly with negligible quality degradation.

Adaptive search range algorithm based on Cauchy distribution

In video coding standard, motion estimation (ME) always plays an important role in reducing temporal redundancies at the expense of higher computational complexity. Many fast ME algorithms have been proposed to reduce the coding complexity. Some papers focus on applying specific search patterns to reduce the search points within a fixed search range (SR). But there are only a few of them trying to reduce the size of SR. In this paper, an adaptive SR algorithm is presented. Cauchy distribution is used to model the SR for one frame and the information of motion vector differences in the neighboring blocks is used to adjust the SR for a particular block. Experimental results show that the proposed algorithm can reduce the size of SR significantly with negligible quality degradation.

Photo album compression By leveraging temporal-spatial correlations and HEVC

The advancing digital photography technology has resulted in a large number of photos stored in personal computers. Photo album compression algorithms aim to save storage space and efficiently manage photos. In this paper, a general forest structure model involving depth constrain for photo album compression is proposed, which further exploits the correlations between images in the photo album. We firstly represent the images as nodes in a graph and directed edges between them as predictive coding relationship. Affinity propagation is then applied to compute for a depth-constrained forest. Finally, we adopt depth-first search algorithm to generate the compression order according to forest structure and HEVC to compress the images with adaptive GOPs and reference list. Experimental results show that the proposed compression method provides much better rate-distortion performance compared to JPEG and significantly reduce the storage space.

Fast sub-pixel motion estimation with simplified modeling in HEVC

Motion estimation (ME) is one of the key elements in video coding standard which eliminates the temporal redundancies between successive frames. In recent international video coding standards, sub-pixel ME is proposed for its excellent coding performance. Compared with integer-pixel ME, sub-pixel ME needs interpolation to get the value in sub-pixel position. Also, Hadamard transform will be applied in order to achieve better performance. Therefore, it is becoming more and more critical to develop fast sub-pixel ME algorithms. In this paper, a novel fast sub-pixel ME algorithm is proposed which makes full use of 8 neighboring integer-pixel points. This algorithm models the error surface in sub-pixel position by a second order function with five parameters two times to predict the best sub-pixel position. Experimental results show that the proposed method can reduce the complexity significantly with negligible quality degradation.

Adaptive Bilateral Filter Considering Local Characteristics

In this paper, we propose a simple but effective adaptive bilateral filter considering local characteristics. The presented method exploits the local gaussian gradient information of the processing image and applies bilateral filter with changing the range filter parameter

Similar images compression based on DCT pyramid multi-level low frequency template

\sigma_{r}

An adaptive motion data storage reduction method for temporal predictor

adaptively. The proposed adaptive bilateral filter could preserve more details of the image compared with original bilateral filter in edge or texture regions. What is more, it is more robust to the noise. Also compared with the original bilateral filter and the state-of-art wavelet de-noising method, the proposed method also can effectively remove signal noise while preserving the original structure.

Modified distortion redistribution problem for High Efficiency Video Coding(HEVC)

Medical imaging applications produce a huge amount of similar images. Instead of compressing each image individually, set redundancy compression (SRC) methods remove the inter image redundancy and reduce storage. However, in the previous SRC methods - MMD, MMP and Centroid methods, the prediction templates for extracting set redundancy are not very efficient, especially when image sets are very large with several clusters. In this paper, inspired by face recognition techniques, a novel lossless SRC method is derived based onDCT pyramid multi-level low frequency template. The approximation subband is used as a prediction template for each image to calculate the residue. Intra prediction is also used to reduce the entropy of the residues. Experiments with 3 sets of MR brain images demonstrate the efficiency of our proposed algorithm in respect to bits/pixel (bpp).

An improved method for color images enhancement considering HVS

In the state-of-art video coding standard HEVC, temporal motion vector (MV) predictor is adopted in order to improve coding efficiency. However, motion vector information in reference frames, which is used by temporal MV predictor, takes significant amount of bits in memory storage. Therefore motion data needs to be compressed before storing into buffer. In this paper we propose an adaptive motion data storage reduction method. First, it divides the current 16x16 block in the reference frame into four partitions. One MV is sampled from each partition and all sampled MVs form a MV candidate set. Then it judges if one or two MVs should be stored into the MV buffer by checking the maximum distance between any two of the MVs in the candidate set. If the maximum distance is greater than a certain threshold, the motion data of the two MVs that have maximum distance are put into memory; otherwise the motion data of the upper left block is stored. The basic goal of the proposed method is to improve the accuracy of temporal MV predictor at the same time reducing motion data memory size. Simulation results show that compared to the original HEVC MV memory compression method in the 4th JCT-VC meeting, the proposed scheme achieves a coding gain of 0.5%~0.6%; and the memory size is reduced by more than 87.5% comparing to without using motion data compression.

Color bleeding reduction in image and video compression

Adaptive quantization matrix design for different block sizes is one of the possible methods to improve the RD performance in video coding and has recently attracted the focus of many researchers. In this paper, we first analyze the shortcomings of the evenly distributed distortion method which was proposed recently. In order to tackle these problems, we propose two modified methods, method I with relaxed distortion constraints and method II is iterative boundary distortion minimization problem considering variance adaptively. Both problems can be solved using convex optimization effectively and efficiently. Simulations have been conducted based on HM4.0, which is the reference software of the latest High Efficiency Video Coding (HEVC). Simulation results show the effect of our proposed methods. Both methods show their significance when evaluated by RD performance.