Extraction of high-resolution video stills from MPEG image sequences

Chen, D.; Schultz, Richard R.

doi:10.1109/icip.1998.723427

Cited by 30 publications

(22 citation statements)

References 5 publications

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“…Many medical types of equipment as the Computer Aided Tomography (CAT), the Magnetic Resonance Images (MRI) or the echography or ultra-sound images, allow the acquisition of several images, which can be combined, in order to obtain a higher resolution image. (v) Improvement of images from compressed video [98][99][100][101]. For example, in [102] the image high frequency information recovery, lost in the compression process, is addressed.…”

Section: Sr Advancesmentioning

confidence: 99%

Advances in video coding for hand-held device implementation in networked electronic media

Núñez

2006

J Real-Time Image Proc

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This survey addresses a number of challenges and research areas identified in real time image processing for state-of-the-art hand-held device implementation in networked electronic media. The challenges appear when having to develop and map processing algorithms not only on fading, noisy, and multi-path band limited transmission channels, but more specifically here on the limited resources available for decoding and scalable rendering on batterylimited hand-held mobile devices. Networked electronic media requires scalable video coding which in turn introduces additional degradation. These problems raise some complex issues discussed in the paper. A need to extend, modify and even create new algorithms and tools, targeting architectures, technology platforms, and design techniques as well as scalability, computational load and energy efficiency considerations has established itself as a key research area. A multidisciplinary approach is advocated.

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Section: Sr Advancesmentioning

confidence: 99%

Advances in video coding for hand-held device implementation in networked electronic media

Núñez

2006

J Real-Time Image Proc

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“…r r g k f t h t k n k = + ∑ n l n n l l (1) where ( , ; , ) r h t k n l is the linear mapping that includes warping, blurring (due to the Point Spread Function (PSF) and down-sampling operations between the discrete HR image ( , ) r f t n and the k-th discrete LR image ( , ), g k l ( , ) n k l is the additive noise.…”

Section: Mathematics Modelmentioning

confidence: 99%

“…There are only a few methods that have been proposed for compressed video. Chen [1] proposed to decompress the MPEG video and then use the SR algorithm for raw video, but the decompression 1 discards important information about the quantization error. Altunbasak [2] utilized the quantization information as the basis for a Projection Onto Convex Set (POCS)-based algorithm, however, all other sources of error are ignored.…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm of Super-Resolution reconstruction for compressed video

Zhu

2007

J. of Electron.(China)

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Super-Resolution (SR) technique means to reconstruct High-Resolution (HR) images from a sequence of Low-Resolution (LR) observations, which has been a great focus for compressed video. Based on the theory of Projection Onto Convex Set (POCS), this paper constructs Quantization Constraint Set (QCS) using the quantization information extracted from the video bit stream. By combining the statistical properties of image and the Human Visual System (HVS), a novel Adaptive Quantization Constraint Set (AQCS) is proposed. Simulation results show that AQCS-based SR algorithm converges at a fast rate and obtains better performance in both objective and subjective quality, which is applicable for compressed video.

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“…However, it is quite sensitive to noise. Chen et al [13] present a Bayesian high-resolution video stills (HRVS) algorithm within MPEG-1 compression standard, which shows some similarities with optical flow estimation. Though it can be extended to MPEG-2, it is mainly for super-resolution enhancement.…”

Section: Introductionmentioning

confidence: 99%

Optical flow approximation based motion object extraction for MPEG-2 video stream

Yang

Chen

Qiya

et al. 2009

J Real-Time Image Proc

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This paper presents a compressed-domain motion object extraction algorithm based on optical flow approximation for MPEG-2 video stream. The discrete cosine transform (DCT) coefficients of P and B frames are estimated to reconstruct DC ? 2AC image using their motion vectors and the DCT coefficients in I frames, which can be directly extracted from MPEG-2 compressed domain. Initial optical flow is estimated with Black's optical flow estimation framework, in which DC image is substituted by DC ? 2AC image to provide more intensity information. A high confidence measure is exploited to generate dense and accurate motion vector field by removing noisy and false motion vectors. Global motion estimation and iterative rejection are further utilized to separate foreground and background motion vectors. Region growing with automatic seed selection is performed to extract accurate object boundary by motion consistency model. The object boundary is further refined by partially decoding the boundary blocks to improve the accuracy. Experimental results on several test sequences demonstrate that the proposed approach can achieve compresseddomain video object extraction for MPEG-2 video stream in CIF format with real-time performance.

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Extraction of high-resolution video stills from MPEG image sequences

Cited by 30 publications

References 5 publications

Advances in video coding for hand-held device implementation in networked electronic media

Advances in video coding for hand-held device implementation in networked electronic media

A novel algorithm of Super-Resolution reconstruction for compressed video

Optical flow approximation based motion object extraction for MPEG-2 video stream

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