Picture Quality-Procedures for Evaluating Subjective Effects of Interference

Fredendall, G.L.; Behrend, William L.

doi:10.1109/jrproc.1960.287504

Cited by 17 publications

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“…Specifically, the quality measures of lossy algorithms can be classified into: Subjective and Objective measures. In the subjective, the compressed image will be evaluated by human observer who judges the quality of the algorithm based on the quality rating in Table 1 (Frendendall and Behrend, 1960):…”

Section: Compression Performance Measuresmentioning

confidence: 99%

Lossy Image Compression by Rounding the Intensity Followed by Dividing (RIFD)

Otair¹,

Shehadeh²

2016

RJASET

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Several millions of digital images are transmitted every minute via mobile applications. The main feature of these images is their huge sizes. However, most of their details are not important such as natural images. Continuous efforts are achieved to utilize the wireless bandwidth and capacity for mobile users. One of the most significant efforts is the image compression. The aim of this study is to introduce a new lossy technique called RFID for compressing images, in order to overcome these problems by achieving high compression ratio. The proposed technique depends on increasing the redundancy and similarity among the neighboring pixels of images by rounding the pixels' intensities followed by the dividing process, which makes compression feasible. It can be applied alone or followed by any lossless compression algorithm. Experimental results show a great performance when RIFD followed by Huffman algorithm.

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Section: Compression Performance Measuresmentioning

confidence: 99%

Lossy Image Compression by Rounding the Intensity Followed by Dividing (RIFD)

Otair¹,

Shehadeh²

2016

RJASET

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“…inferior) [2] are selected as the reference images for the various a distortion processes. For defocusing blur, the distorted image generated by 7 x 7 lowpass smoothing mask is selected as the reference image, i.e.g,,7(x,y).…”

Section: Additive Mixing Of the Distorted And The Original Imagementioning

confidence: 99%

Modelling of image distortion processes associated in video-phone systems and descriptors for image quality

Hwee

Arichandran²

Proceedings of IEEE Singapore International Conference on Networks and International Conference on Information Engineering '95

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A variety of video-phone systems have begun to appear in the consumer market. The quality of the images generated by these systems is severely affected by the bandwidth restriction of the twisted cable pair. Typically, some of these systems operate with a transmission bit rate less than 19.2 kbps. In comparing different systems with respect to image qualiv, one j n & it useful to assess the quality in terms of descriptors that would represent the subjective assessment more naturally. In this paper, we investigate the possible use of fuzzy set descriptor for the video-phone image. In order to do this, we examine the types of image distortion processes that are obtained in such video-phone systems. We consider three physical effects, name+ blurring due to defocusing, blurring due to motion of subject, additive electrical noise in the transmission systems. In addition to the physical distortion processes, we consider distortion due to sofrware artifacts, mainly arising @om the use of lossy compression algorithms as well as the associated image blocking effects.

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2011

Practical Image and Video Processing Using MATLAB®

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Picture Quality-Procedures for Evaluating Subjective Effects of Interference

Cited by 17 publications

References 0 publications

Lossy Image Compression by Rounding the Intensity Followed by Dividing (RIFD)

Lossy Image Compression by Rounding the Intensity Followed by Dividing (RIFD)

Modelling of image distortion processes associated in video-phone systems and descriptors for image quality

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