By Yun Q. Shi, Huifang Sun
Multimedia nonetheless can't accommodate the call for for big quantities of visible info. with no the iteration of high quality video bitstreams, restricted services will proceed to stifle the development of multimedia applied sciences. Thorough grounding in coding is required in order that purposes corresponding to MPEG-4 and JPEG 2000 might come to fruition.
Image and Video Compression for Multimedia Engineering offers a pretty good, finished figuring out of the basics and algorithms that bring about the production of latest equipment for producing top of the range video bit streams. The authors current a couple of appropriate advances besides foreign criteria.
New to the second one version
· A bankruptcy describing the lately built video coding average, MPEG-Part 10 Advances Video Coding often referred to as H.264
· basic thoughts and algorithms of JPEG2000
· colour structures of electronic video
· up to date video coding criteria and profiles
Visual information, snapshot, and video coding will proceed to let the construction of complicated undefined, appropriate to the calls for of latest purposes. overlaying either picture and video compression, this booklet yields a distinct, self-contained reference for practitioners tobuild a foundation for destiny examine, study, and development.
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Extra resources for Image and video compression for multimedia engineering: fundamentals, algorithms, and standards
First, a few features common to all three major techniques discussed in Chapters 11 through 13 are addressed. They are the aperture and ill-posed inverse problems, conservation and neighborhood information, occlusion and disocclusion, and rigid and nonrigid motion. Second, a variety of different classiﬁcations of motion estimation techniques are presented. Frequency domain methods are discussed as well. Third, performance comparison between three major techniques in motion estimation is made. Finally, the new trends in motion estimation are presented.
Owing to frequency masking, in the transform domain, say, the discrete cosine transform (DCT) domain, we can drop some high-frequency coefﬁcients with small magnitudes to achieve data compression without noticeably affecting the perception of the HVS. This leads to a technique called transform coding, discussed in Chapter 4. 4 Temporal Masking Temporal masking is another picture-independent feature of the HVS. It states that it takes a while for the HVS to adapt itself to the scene when the scene changes abruptly.
Therefore, the ratio between the required bit rate and the largest possible bit rate is about 1289. This implies that we have to compress the video data by at least 1289 times in order to accomplish the transmission described in this example. Note that an audio signal has not been accounted for yet in this illustration. With increasingly demanding video services, such as three-dimensional (3-D) movies and games, and high video quality, such as HDTV, advanced image, and video data compression is necessary.
Image and video compression for multimedia engineering: fundamentals, algorithms, and standards by Yun Q. Shi, Huifang Sun