Region-of-interest video coding based on rate and distortion variations for H.263+

Chi, Ming-Chieh; Chen, Mei-Juan; Yeh, Chia‐Hung; Jhu, Jyong-An

doi:10.1016/j.image.2007.12.001

Cited by 28 publications

(19 citation statements)

References 10 publications

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“…The other class of visual attention models is top-down processing [20][21][22][23][24][25]12]. The top-down visual attention models are more frequently applied to video applications, since they are more correlated with human attractiveness.…”

Section: Automatic Identificationmentioning

confidence: 99%

Weight-based R-λ rate control for perceptual HEVC coding on conversational videos

Deng

et al. 2015

Signal Processing: Image Communication

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Section: Automatic Identificationmentioning

confidence: 99%

Weight-based R-λ rate control for perceptual HEVC coding on conversational videos

Deng

et al. 2015

Signal Processing: Image Communication

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“…In fact, to extract interesting regions from a video, we need to analyze the video and then create a visual attention model [4,5,8,27]. However, poor lighting conditions may make visual cue extraction difficult and then obtain an incorrect visual attention model.…”

Section: System Descriptionmentioning

confidence: 99%

Video quality enhancement based on visual attention model and multi-level exposure correction

Lin

2015

Multimed Tools Appl

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Due to unfavorable environmental conditions such as lack of lighting, poor visual quality in images and videos may make intelligent image/video systems unstable. This means that visual quality enhancement plays an important role in image/video processing, computer vision, and pattern recognition. In this paper, we propose a video quality enhancement scheme based on visual attention model and multi-level exposure correction. To this end, the proposed scheme is composed of four parts: pre-processing, visual attention model generation, multi-level exposure correction, and temporal filtering. To extract more visual cues for visual attention model generation, a pre-processing is used to modify each frame. After preprocessing, facial and non-facial cues are measured to generate visual attention maps of each frame. On the basis of visual attention maps, a multi-level exposure correction algorithm is utilized to adjust the exposure level of each frame and then create several intermediate results. After fusing intermediate results, a synthesized image with good visual quality can be obtained. To avoid flicker effect, a temporal filter is exploited to make the variance of the exposure level small in the temporal domain. To evaluate the performance of the proposed scheme, some images/videos captured by mobile phones and digital cameras are tested. The experimental results show that the proposed scheme can effectively deal with the images/videos with low and high exposure levels. The results also demonstrate that the proposed scheme outperforms some existing methods in terms of visual quality.

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“…Top-down attention has been more popularly employed because of its intuitiveness. Frequently used top-down cues include faces [20], [21], skin regions [22], [23], moving objects [3], [24], etc. Bottom-up and top-down attention models can be combined for more accurate attention modeling.…”

Section: B Foveated Video Codingmentioning

confidence: 99%

Subjective Quality Evaluation of Foveated Video Coding Using Audio-Visual Focus of Attention

Lee

Simone

Ebrahimi

2011

IEEE J. Sel. Top. Signal Process.

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Abstract-This paper presents a foveated coding method using audio-visual focus of attention and its evaluation through extensive subjective experiments on both standard-definition and high-definition sequences. Regarding a sound-emitting region as the location drawing the human attention, the method applies varying quality levels in an image frame according to the distance of a pixel to the identified sound source. Two experiments are presented to prove the efficiency of the method. Experiment 1 examines the validity and effectiveness of the method in comparison to the constant quality coding for high-quality conditions. In Experiment 2, the method is compared to the fixed bit rate coding for low quality conditions where coding artifacts are noticeable. The results demonstrate that the foveated coding method provides considerable coding gain without significant quality degradation, but uneven distributions of the coding artifacts (blockiness) by the method are often less preferred than the uniform distribution of the artifacts. Additional interesting findings are also discussed, such as content dependence of the performance of the method, the memory effect in multiple viewings, and the difference in the quality perception for frame size variations.Index Terms-Audio-visual focus of attention, content dependence, foveated coding, H.264/AVC, memory effect, quality of experience, subjective quality assessment.

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Region-of-interest video coding based on rate and distortion variations for H.263+

Cited by 28 publications

References 10 publications

Weight-based R-λ rate control for perceptual HEVC coding on conversational videos

Weight-based R-λ rate control for perceptual HEVC coding on conversational videos

Video quality enhancement based on visual attention model and multi-level exposure correction

Subjective Quality Evaluation of Foveated Video Coding Using Audio-Visual Focus of Attention

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