Jia Su

Reversed intra prediction based on chroma extraction in HEVC

In the HEVC standard, UIP (unified intra prediction) is included as one of the new coding tools in the latest HM software, which supports 33 different directions in addition to the DC prediction mode. It represents directional structures more accurately, and performs remarkable bit rate saving. However, UIP also significantly increases the burdens on computational complexity. This contribution proposes a reversed intra prediction which utilizes the inherent correlation between luma and chroma. By reversing the prediction orders, the chroma prediction is executed first in order to create the depth map and mode candidate subset that will reduce the computational complexity in luma intra prediction. Experiment results show that the proposed algorithm can achieve approximately 30% time savings in average with 0.03, 0.05 BD-PSNR losses in chroma components and unnoticeable increments in bit rate.

Motion detection based motion estimation algorithm for video surveillance application

Large amounts of data have to be processed, transmitted and storage by modern video surveillance applications. Thus, low bit-rate video compressed algorithm is very critical. For indoor surveillance application, only motive object is the most interesting and significant part. This paper proposed a motion detection based motion estimation algorithm to achieve low bit-rate and low complexity for surveillance application. It includes a pre-stage for classifying macroblocks into motive and static blocks. For the unimportant static macroblock, low bitrate skip mode has been chosen as the best mode. Simulation results for indoor surveillance videos obtained 22%-28% bit-rate saving and 10–36% ME time saving with no significant video quality loss.

Multi objective optimization based fast motion detector

A large number of surveillance applications require fast action, and since many surveillance applications, motive objects contain most critical information. Fast detection algorithm system becomes a necessity. A problem in computer vision is the determination of weights for multiple objective function optimizations. In this paper we propose techniques for automatically determining the weights, and discuss their properties. The Min-Max Principle, which avoids the problems of extremely low or high weights, is introduced. Expressions are derived relating the optimal weights, objective function values, and total cost. Simulation results show, compared to the conventional work, it can achieve around 40% time saving and higher detection accuracy for both outdoor and indoor surveillance videos.

Low Bit-Rate Motion Block Detection for Uncompressed Indoor Surveillance

A large number of surveillance applications requires narrow channel transmission and limited capacity storage, since very low bit-rate techniques becomes necessary. For many surveillance applications, motive objects contain most interest information. This paper proposed an enhanced motion block detector, which includes a pre-stage for adaptively classifying macroblocks into motive and static blocks. In this pre-stage, linear divided difference filter and weighted erosion filter are proposed to release from the foreground clash and lightening noise respectively. For the unimportant static macroblock, low bit-rate skip mode has been chosen as the best mode. Simulation results shows, compared to the conventional work, it can achieve 24%-38% bit-rate saving, 8%-44% ME time saving and higher detection accuracy for uncompressed surveillance videos.

Adaptive fast DIRECT mode decision algorithm using mode and Lagrangian cost prediction for B frame in H.264/AVC

In this paper, a fast spatial DIRECT mode decision method for B frame in H.264/AVC is proposed. It is based on a statistical analysis on multiple video sequences, and the strong relationship of mode selection and rate-distortion (RD) cost between the current DIRECT macroblock (MB) and the co-located MBs is observed. With the check of mode condition and adaptive threshold of RD cost, the complex mode decision process can be released at an early stage even for small QP cases. Simulation results demonstrate the proposed method can achieve much better performance than the original exhaustive rate-distortion optimization (RDO) based mode decision algorithm by reducing up to 57.1 % of motion estimation (ME) time for IBPBP picture group with only negligible bit increment and quality degradation.

Coarse to fine adaptive interpolation filter for high resolution video coding

With the increasing demand of high video quality and large image size, adaptive interpolation filter (AIF) addresses these issues and conquers the time varying effects resulting in increased coding efficiency, comparing with recent H.264 standard. However, currently most AIF algorithms are based on either frame level or macroblock (MB) level, which are not flexible enough for different video contents in a real codec system. And most of them are facing a severe time consuming problem. This paper proposes a content based coarse to fine AIF algorithm, which can adapt to video contents by adding different filters and conditions from coarse to fine. The overall algorithm has been mainly made up by 3 schemes: frequency analysis based frame level skip interpolation, motion vector modeling based region level interpolation, and edge detection based macroblock level interpolation. The experimental results show that the proposed algorithm is able to reduce total encoding time about 41% for 720p and 25% for 1080p sequences averagely, comparing with key technology areas (KTA) Enhanced AIF algorithm, while obtains a BD-PSNR gain up to 0.004 and 3.122 BDBR reduction.