Efficient Error Concealment Algorithm Research Articles

Efficient Spatial-Temporal Error Concealment Algorithm and Hardware Architecture Design for H.264/AVC

This paper presents an efficient error concealment algorithm for video bitstream over error-prone channel suffering from damage. Moreover, hardware architecture design and chip implementation of the proposed error concealment algorithm are also presented. For spatial error concealment, a mode selection algorithm considering the reuse of intra mode information embedded in bitstream is developed for the adaptation of bilinear and directional interpolation. It suffers only 0.08 dB video quality drop in average but the speedup measured on a general purpose processor is up to 40 times compared with the conventional methods. It is also more suitable for low cost hardware design. For temporal error concealment, the decoded motion vectors of the neighboring blocks of the corrupted macroblock are reused to provide hints to estimate the motion vector of the corrupted macroblock. Moreover, for real-time applications, a data and computational results reuse scheme of motion vector estimation is proposed and 96% computation and memory bandwidth can be reduced compared with the conventional methods with 0.18 dB quality drop in average. With the UMC 90 nm 1P9M process, the proposed error concealment engine can process HDTV1080P 30 frames per second video data and the power consumption is 15.77 mW at 125 MHz operation frequency.

Error concealment of multi-view video sequences using inter-view and intra-view correlations

An efficient error concealment algorithm for multi-view video sequences is proposed in this work. First, we develop three concealment modes: temporal bilateral error concealment (TBEC), inter-view bilateral error concealment (IBEC), and multi-hypothesis error concealment (MHEC). TBEC and IBEC, respectively, exploit intra-view and inter-view correlations in multi-view video sequences to reconstruct an erroneous block. MHEC finds a few candidate blocks based on the block matching principle and combines them for the concealment. We then propose a mode selection scheme, which chooses one of the three modes adaptively to provide reliable and accurate concealment results. Simulation results demonstrate that the proposed algorithm can protect the quality of reconstructed videos effectively even in severe error conditions.

VLSI Implementation of High-Performance Error Concealment Processor for TV Broadcasting

This paper presents an error concealment processor to increase the performance of the TV receiver while the decoding bit stream over error-prone channel suffers from damage. An efficient error-concealment algorithm is advised with the adaptation of the spatial interpolation and the temporal prediction to reduce the nonmatched error for high motion regions and achieve fine resolution for still or low motion regions. Based on the adaptive algorithm, we proposed a parallel VLSI architecture with pipeline scheduling for real time implementation. The error concealment processor consists of the computational core, RAM block and interface, and then is integrated to the video decoder by using a complex processing schedule. The computational sources are commonly used for various frame types processing to reduce the hardware cost. The chip occupies about 27 k gates and includes one on-chip line-buffer. The silicon area is about 9 mm2 and the throughput rate can achieve 50 Mpixels/s, when implemented by 0.35-mum CMOS technology.

Temporal Error Concealment for MPEG Coded Video Using a Self-Organizing Map

The performance of MPEG video transmission over error-prone channels is limited by the channel noise. An efficient error concealment (EC) scheme is essential for diminishing the impact of transmission errors in a compressed video, A number of EC techniques have been developed to combat the transmission errors. However, the previous techniques are always inefficient when the motions of video object are fast or complex. This paper proposes a novel adaptive EC algorithm to conceal the error for block and motion-compensation based video coding systems. The proposed EC method employs an unsupervised artificial neural network (ANN) model, i.e. self-organizing map (SOM), as a predictor to estimate the motion vectors of the damaged macroblocks (MBs). Then the estimated motion vectors were utilized to reconstruct the damaged MB by exploiting the spatial information from reference frames based on the boundary matching criterion. Because of the SOM has a great capacity for visualizing and interpreting high-dimensional data sets, the estimation model proposed herein can exploit the nonlinearity property of the SOM to estimate lost motion vectors more accurately. Computer simulations show that the visual quality and the PSNR evaluation of reconstructed frames are significantly improved by using the proposed EC algorithm. Thus, the proposed algorithm is expected to be practical for motion vector compressed video in error-prone networks.

Error Concealment Using Adaptive Multilayer Perceptrons (MLPs) for Block-Based Image Coding

Image coding algorithms such as Vector Quantisation (VQ), JPEG and MPEG have been widely used for encoding image and video, These compression systems utilise block-based coding techniques to achieve a higher compression ratio. However, a cell loss or a random bit error during network transmission will permeate into the whole block, and then generate several damaged blocks. Therefore, an efficient Error Concealment (EC) scheme is essential for diminishing the impact of damaged blocks in a compressed image. In this paper, a novel adaptive EC algorithm is proposed to conceal the error for block-based image coding systems by using neural network techniques in the spatial domain. In the proposed algorithm, only the intra-frame information is used for reconstructing the image with damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the PSNR evaluation of a reconstructed image are significantly improved using the proposed EC algorithm.