491 research outputs found
Current video compression algorithms: Comparisons, optimizations, and improvements
Compression algorithms have evolved significantly in recent years. Audio, still image, and video can be compressed significantly by taking advantage of the natural redundancies that occur within them. Video compression in particular has made significant advances. MPEG-1 and MPEG-2, two of the major video compression standards, allowed video to be compressed at very low bit rates compared to the original video. The compression ratio for video that is perceptually lossless (losses can\u27t be visually perceived) can even be as high as 40 or 50 to 1 for certain videos. Videos with a small degradation in quality can be compressed at 100 to 1 or more; Although the MPEG standards provided low bit rate compression, even higher quality compression is required for efficient transmission over limited bandwidth networks, wireless networks, and broadcast mediums. Significant gains have been made over the current MPEG-2 standard in a newly developed standard called the Advanced Video Coder, also known as H.264 and MPEG-4 part 10. (Abstract shortened by UMI.)
A perceptual comparison of empirical and predictive region-of-interest video
When viewing multimedia presentations, a user only
attends to a relatively small part of the video display at any one point in time. By shifting allocation of bandwidth from peripheral areas to those locations where a userâs gaze is more likely to rest, attentive displays can be produced. Attentive displays aim to reduce resource requirements while minimizing negative user perceptionâunderstood in this paper as not only a userâs ability to assimilate and understand information but also his/her subjective satisfaction with the video content. This paper introduces and discusses a perceptual comparison between two region-of-interest display (RoID) adaptation techniques. A RoID is an attentive display where bandwidth has been preallocated around measured or highly probable areas of user gaze. In this paper, video content was manipulated using two sources of data: empirical measured data (captured using eye-tracking technology) and predictive data (calculated from the physical characteristics of the video data). Results show that display adaptation causes significant variation in usersâ understanding of specific multimedia content. Interestingly, RoID adaptation and the type of video being presented both affect user perception of video quality. Moreover, the use of frame rates less than 15 frames per second, for any video adaptation technique, caused a significant reduction in user perceived quality, suggesting that although users are aware of video quality reduction, it does impact level of information assimilation and understanding. Results also highlight that user level of enjoyment is significantly affected by the type of video yet is not as affected by the quality or type of video adaptationâan interesting implication in the field of entertainment
Critical Data Compression
A new approach to data compression is developed and applied to multimedia
content. This method separates messages into components suitable for both
lossless coding and 'lossy' or statistical coding techniques, compressing
complex objects by separately encoding signals and noise. This is demonstrated
by compressing the most significant bits of data exactly, since they are
typically redundant and compressible, and either fitting a maximally likely
noise function to the residual bits or compressing them using lossy methods.
Upon decompression, the significant bits are decoded and added to a noise
function, whether sampled from a noise model or decompressed from a lossy code.
This results in compressed data similar to the original. For many test images,
a two-part image code using JPEG2000 for lossy coding and PAQ8l for lossless
coding produces less mean-squared error than an equal length of JPEG2000.
Computer-generated images typically compress better using this method than
through direct lossy coding, as do many black and white photographs and most
color photographs at sufficiently high quality levels. Examples applying the
method to audio and video coding are also demonstrated. Since two-part codes
are efficient for both periodic and chaotic data, concatenations of roughly
similar objects may be encoded efficiently, which leads to improved inference.
Applications to artificial intelligence are demonstrated, showing that signals
using an economical lossless code have a critical level of redundancy which
leads to better description-based inference than signals which encode either
insufficient data or too much detail.Comment: 99 pages, 31 figure
- âŠ