A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Processing and Communications
Video Processing and Communications
Region-based rate control and bit allocation for wireless video transmission
IEEE Transactions on Multimedia
Visual sensitivity guided bit allocation for video coding
IEEE Transactions on Multimedia
A Novel 4-D Perceptual Quantization Modeling for H.264 Bit-Rate Control
IEEE Transactions on Multimedia
Foveated video compression with optimal rate control
IEEE Transactions on Image Processing
Automatic foveation for video compression using a neurobiological model of visual attention
IEEE Transactions on Image Processing
Low bit-rate coding of image sequences using adaptive regions of interest
IEEE Transactions on Circuits and Systems for Video Technology
Overview of the H.264/AVC video coding standard
IEEE Transactions on Circuits and Systems for Video Technology
On Lagrange multiplier and quantizer adjustment for H.264 frame-layer video rate control
IEEE Transactions on Circuits and Systems for Video Technology
Region-of-Interest Based Resource Allocation for Conversational Video Communication of H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
A scheme for attentional video compression
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Visual saliency detection with center shift
Neurocomputing
Saliency detection using joint spatial-color constraint and multi-scale segmentation
Journal of Visual Communication and Image Representation
An edge detection with automatic scale selection approach to improve coherent visual attention model
Pattern Recognition Letters
Visual saliency guided video compression algorithm
Image Communication
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A visual attention-based bit allocation strategy for video compression is proposed. Saliency-based attention prediction is used to detect interesting regions in video. From the top salient locations from the computed saliency map, a guidance map is generated to guide the bit allocation strategy through a new constrained global optimization approach, which can be solved in a closed form and independently of video frame content. Fifty video sequences (300 frames each) and eye-tracking data from 14 subjects were collected to evaluate both the accuracy of the attention prediction model and the subjective quality of the encoded video. Results show that the area under the curve of the guidance map is 0.773+/-0.002, significantly above chance (0.500). Using a new eye-tracking-weighted PSNR (EWPSNR) measure of subjective quality, more than 90% of the encoded video clips with the proposed method achieve better subjective quality compared to standard encoding with matched bit rate. The improvement in EWPSNR is up to over 2dB and on average 0.79dB.