Fractional-pixel Interpolation Research Articles

A Distortion-Aware Multi-Task Learning Framework for Fractional Interpolation in Video Coding

Motion-compensated prediction adopts fractional-pixel interpolation to obtain the best motion vector. Traditional fixed interpolation filters cannot handle various content and structures well, and existing convolutional neural network based methods cannot fully exploit the distortion characteristics for fractional interpolation. Therefore, this paper proposes a distortion-aware multi-task learning framework (DA-MLF) to perform fractional interpolation. First, a multi-task training framework is proposed to provide the distortion characteristics as complementary information for improving the performance of subsequent interpolation. Then, a uniform interpolation sub-network is proposed to accomplish fractional interpolation, which utilizes the feature fusion module to fuse abundant local features, and the distortion awareness module to capture the multi-scale information of compression artifacts. Furthermore, DA-MLF is integrated into High Efficiency Video Coding (HEVC) test model, and multiple experiments are performed to evaluate the effectiveness of our method. On HEVC testing sequences, DA-MLF achieves 5.0%, 4.0% and 1.7% BD-rate reduction on average compared to the HEVC baseline, under low-delay P, low-delay B and random-access configurations, respectively. The experimental results validate that our framework not only achieves the best interpolation performance but also has the lowest computational complexity compared with state-of-the-art methods.

Challenging the Best HEVC Fractional Pixel FPGA Interpolators With Reconfigurable and Multifrequency Approximate Computing

Applicable in different fields and markets, low energy high efficiency video coding (HEVC) codecs and their constituting elements have been heavily studied. Fractional pixel interpolation is one of its most costly blocks. In this letter, a field programmable gate array implementation of HEVC fractional pixel interpolation, outperforming literature solutions, is proposed. Approximate computing, in conjunction with hardware reconfiguration, guarantees a tunable interpolation system offering an energy versus quality tradeoff to further reduce energy.

High Efficiency Video Coding

In recent years,with the development of video coding technology and the increasing popularity of high definition(HD)video content,the ISO/IEC Moving Picture Experts Group(MPEG)and the ITU-T Video Coding Experts Group(VCEG)formed the joint collaborative team on video coding(JCV-VC)in 2010which aims to develop the next-generation video coding standard for HD video application,called High Efficiency Video Coding(HEVC).So far the test model HM has been used for performance evaluation and algorithm test.The final international standard of HEVC has been completed and published in the end of 2012based on the working plan of JCV-VC.In this paper,the key technologies about HEVC will be surveyed,specifically including the quad-tree structure of coding unit,prediction unit and transform unit,the advanced motion vector prediction and merging technology,angular intra prediction,DCT-based fractional pixel interpolation filter and context adaptive arithmetic coding etc.Finally the coding performance of some coding tools and complexity of HEVC are analyzed in detail.

A Single-Pass-Based Localized Adaptive Interpolation Filter for Video Coding

Recently, an efficient coding tool named adaptive interpolation filtering (AIF) has been proposed to hybrid video coding scheme. By introducing Wiener filter into the fractional-pixel interpolation procedure, AIF can reduce the inter-prediction error and improve coding efficiency significantly. However, the training-based Wiener filter mechanism brings AIF an inherent multi-pass encoding structure, which imposes big burdens on the encoder in terms of huge computational complexity and memory access. In this paper, we propose a single-pass-based localized adaptive interpolation filtering (SPL-AIF) algorithm for video coding, which can reduce the complexity of AIF dramatically without sacrifice of its outstanding coding performance. The proposed SPL-AIF algorithm is based on the observation that there is a high correlation among optimal interpolation filters of consecutive frames, and different regions in a frame often possess different statistical characteristics. Accordingly, the proposed algorithm can be designed including two major parts. First, a competitive filter set which includes the optimal interpolation filters of several previous frames as well as the fixed H.264/AVC interpolation filters is built up for the coding of the current frame. Then a rate-distortion optimization criterion is used to select the best one at macroblock (MB) level. In order to reduce overhead, a predictive coding method is used to compress the filter signaling flag for each MB. Experimental results show that, by using the proposed algorithm, the encoding complexity can be reduced significantly while the average coding gain in Bjontegaard distortion bit-rate reduction can be improved about 1% compared with the multi-pass AIF. The proposed method has been adopted into the Video Coding Expert Group Key Technology Area software.

On Combining Fractional-Pixel Interpolation and Motion Estimation: A Cost-Effective Approach

The additional complexity of the adoption of fractional-pixel motion compensation technology arises from two aspects: fractional-pixel interpolation (FPI) and fractional-pixel motion estimation (FPME). Different from current fast algorithms, we use the internal link between FPME and FPI as a factor in considering optimization by integrally manipulating them rather than attempting to speed them up separately. In this paper, a refinement search order for FPME is proposed to satisfy the criteria of cost/performance efficiency. And then, some strategies, i.e., FPME skipping, early termination and search pattern pruning, are also given for reducing the number of search positions with negligible coding loss. We also propose a FPI algorithm to save redundant interpolation as well as reduce duplicate calculation. Experimental results show that our integrated algorithm significantly improves the overall speed of FPME and FPI. Compared with the FFPS+XFPI and CBFPS+XFPI, the proposed algorithm has already reduced the speed by a factor of 65% and 32%. Additionally, our FPI algorithm can be used to cooperate with any fast FPME algorithms to greatly reduce the computational time of FPI.

A fast quarter-pixel motion estimation algorithm for H.264/AVC

A fast quarter-pixel motion estimation algorithm is proposed in this paper. The proposed algorithm based on mathematical models of the motion compensated prediction errors. Unlike conventional quarter-pixel accurate motion estimation algorithm, proposed algorithm can avoid fractional-pixel interpolation and subsequent fractional-pixel search after integer-precision motion estimation. Experiments show that the proposed algorithm greatly reduces the computational complexity of quarterpixel motion estimation, while keeping the nearly equal quality of the image

Multispectral data compression using bidirectional interband prediction

The introduction of high spatial and spectral resolution sensors on-board remote-sensing spacecraft has increased, by orders of magnitude, the data rates which need to be sustained on the down-link or cross-link transmission channels. Since these channels are severely limited in capacity, the need arises to perform on-board compression to reduce the volume of data which would need to be down-linked. This paper discusses the development and refinement of a low complexity lossy spectral/spatial compression method which provides high compression ratios at low levels of distortion. The developed techniques uses pixels in adjacent bands to predict the intensity of pixels in the band being compressed via a simple linear prediction model. This prediction method when combined with a low-distortion discrete cosine transform (DCT) block coding method yields performance comparable to block-adaptive Karhunen-Loeve Transform (KLT)-DCT methods without incurring the complexity penalty of the KLT. The methods' performance suffers under misregistration. A fractional-pixel interpolation enhancement to the basic technique significantly improves the performance in the case of misregistered bands.

Affinity-Weighted Neural Network Motion Estimation for MPEG Coding

A neural network based motion-estimation technique is developed that is applicable to small as well as large movements. In addition to yielding highly accurate results, a benefit of this technique is the elimination of fractional-pixel interpolation and corresponding search. The technique is formulated in terms of a modified Hopfield neural network. The procedure consists of two stages: estimation of the neural network parameters from the present and previous frame, followed by the estimation of the affinity weights for the candidate macroblocks. The reconstructed macroblock then consists of the sum of the candidate macroblocks weighted by the affinity values. Experiments indicate that this technique produces a 5 dB better peak signal-to-noise ratio (PSNR) than the full search algorithm, 6 dB better than the logarithmic block matching algorithms, and 2 to 3 dB better than some of the very recent motion estimation algorithms. Further, due to the neural network′s fault-tolerant nature and inherent parallelism, potential exists for VLSI implementation as an interconnection of small cells on a single chip.