Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain

Li, Zhi; Lu, Yu; Zhang, Zhaoyang

doi:10.1016/j.jvcir.2007.02.002

Cited by 42 publications

(26 citation statements)

References 17 publications

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“…Liu et al [43] segmented video objects in the H.264 compressed domain exploiting cue only from MV field. Initially, they performed temporal and spatial normalization of the MV field.…”

Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

A survey on compressed domain video analysis techniques

Babu

Tom

Wadekar

2014

Multimed Tools Appl

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Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

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“…Liu et al [43] segmented video objects in the H.264 compressed domain exploiting cue only from MV field. Initially, they performed temporal and spatial normalization of the MV field.…”

Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

A survey on compressed domain video analysis techniques

Babu

Tom

Wadekar

2014

Multimed Tools Appl

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“…We use the magnitudes of the x and y components of the MV's in our analysis. The MV can be preprocessed to account for global motion [2,3]. In an MB that is coded with multiple coding sub-blocks, we take the maximum value of the MV components to determine the component values of MB, as shown below.…”

Section: Slice Group Orderingmentioning

confidence: 99%

“…These types of techniques typically rely on a segmentation step as preprocessing, which may be done efficiently in the compressed domain (e.g., [2]). A motion assisted rate control based on pre-filtered and global motion compensated Motion Vectors (MV) is presented in [3] which is found to enhance the subjective quality of regions with high visual sensitivity.…”

Section: Introductionmentioning

confidence: 99%

An enhanced rate control scheme with motion assisted slice grouping for low bit rate coding in H.264

Kannur

2008

2008 15th IEEE International Conference on Image Processing

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This paper presents an enhanced rate control scheme for H.264 using motion detection and motion analysis. PSNR based measure is used for determining motion complexity and detecting scene changes. Motion Vectors and Rate Distortion measures are further used to classify regions with motion inside a frame and Macro-blocks are then grouped into slices using the Flexible Macro-block Ordering feature of H.264. We present a filtered slice grouping technique to maintain a stable region of motion. The proposed method provides smoother quality videos with better average PSNR. The bit rates are within bound and fewer frames are skipped in sequences with scene changes at low bit rates.

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“…According to the properties of the human visual system (HVS), the ROI areas are usually defined by skin color, contrast, video content, objects, and motion information. Liu et al [14] extract moving objects in the compressed domain by considering global motion compensation, correspondence matrix, and temporal tracking. Mak and Cham [15] identify the background motion and then analyze the decoded motion data to complete the video object segmentation.…”

Section: Introductionmentioning

confidence: 99%

Region-of-interest determination and bit-rate conversion for H.264 video transcoding

Huang

Chen

Tai

et al. 2013

EURASIP J. Adv. Signal Process.

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This paper presents a video bit-rate transcoder for baseline profile in H.264/AVC standard to fit the available channel bandwidth for the client when transmitting video bit-streams via communication channels. To maintain visual quality for low bit-rate video efficiently, this study analyzes the decoded information in the transcoder and proposes a Bayesian theorem-based region-of-interest (ROI) determination algorithm. In addition, a curve fitting scheme is employed to find the models of video bit-rate conversion. The transcoded video will conform to the target bit-rate by re-quantization according to our proposed models. After integrating the ROI detection method and the bit-rate transcoding models, the ROI-based transcoder allocates more coding bits to ROI regions and reduces the complexity of the re-encoding procedure for non-ROI regions. Hence, it not only keeps the coding quality but improves the efficiency of the video transcoding for low target bit-rates and makes the real-time transcoding more practical. Experimental results show that the proposed framework gets significantly better visual quality.

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Real-time spatiotemporal segmentation of video objects in the H.264 compressed domain

Cited by 42 publications

References 17 publications

A survey on compressed domain video analysis techniques

A survey on compressed domain video analysis techniques

An enhanced rate control scheme with motion assisted slice grouping for low bit rate coding in H.264

Region-of-interest determination and bit-rate conversion for H.264 video transcoding

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