International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Video Compression Using Block By Block Basis Salience Detection 1 P.Muthuselvi
M.Sc.,M.E., ,
2
T.Vengatesh MCA.,M.Phil.,(Ph.D).,
1 Assistant
Professor,VPMM Arts & Science college for women, Krishnankoil 626149,Virudhunagar(Dt),Tamilnadu,India.
2 Assistant
Professor,VPMM Arts & Science college for women,Krishnankoil626149,Virudhunagar(Dt),Tamilnadu,India.
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Abstract - This dissertation explores the concept of visual
saliency a measure of propensity for drawing visual attention and presents a variety of novel methods for utilization of visual saliency in video compression and transmission. While the region-of-interest (ROI)-based video coding, ROI parts of the frame are encoded with higher quality than non-ROI parts. It was reducing salient coding artifacts in non-ROI parts of the frame in order to keep user’s attention on ROI. Unfortunately, the salient coding of non-ROI compression parts is uneasy to find the block-by-block basis. The proposed method aims at reducing salient coding artifacts in non-ROI parts by optimizing the saliency-related Lagrange parameter, possibly on a block-by-block basis. Experimental results indicate that the proposed method is able to improve visual quality of encoded video relative to conventional rate distortion optimized block-by-block basis video coding, as well as two state-of-the art perceptual video coding methods. Key Words: ROI-based video coding, attention-grabbing coding artifacts, non-ROI parts, salient coding artifact
1.INTRODUCTION Lossy image and video encoders are known to produce undesirable compression artifacts at low bit rates [1], [2]. Blocking artifacts are the most common form of compression artifacts in block-based video compression. When coarse quantization is combined with motion-compensated prediction, blocking artifacts propagate into subsequent frames and accumulate, causing structured high-frequency noise or motion-compensated edge artifacts that may not be located at block boundaries, and so cannot be attenuated by de-blocking filters that mostly operate on block boundaries [2]. Such visual artifacts may become very severe and attention-grabbing (salient), especially in low-textured regions.
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Recently, region-of-interest (ROI) coding of video using computational models of visual attention [3] has been recognized as a promising approach to achieve highperformance video compression [4]–[7]. The idea behind most of these methods is to encode an area around the predicted attention grabbing (salient) regions with higher quality compared to other less visually important regions. Such a spatial prioritization is supported by the fact that only a small region of 2–5◦ of visual angle around the center of gaze is perceived with high spatial resolution due to the highly non-uniform distribution of photoreceptors on the human retina [4]. ROI-based processing can also be employed in the context of video transmission to combat the effects of transmission channel errors. For instance, ROI parts of the frame can be protected heavily (e.g., by using stronger channel codes) than non-ROI parts of the frame, so that in the case of channel errors or losses, important parts of the frame can still be decoded correctly. In this case, also, ROI could be detected either based on direct eye-tracking measurement or based on visual saliency models.
2. RELATED WORK A. The IKN Saliency Model Among the existing bottom-up computational models of visual attention, the Itti-Koch-Niebur (IKN) model [3] is one of the most well-known and widely used. In this model, the visual saliency is predicted by analyzing the input image through a number of pre-attentive independent feature channels, each locally sensitive to a specific low-level visual attribute, such as local opponent color contrast, intensity contrast, and orientation contrast. More specifically, nine spatial scales are created using dyadic Gaussian pyramids, which progressively low-pass filter and down-sample the input image, yielding an image-size-
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