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correcting for noise and camera irregularities) can be done on the camera interface. Some advanced camera interfaces support these algorithmic operations.

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System Bus

DMA C o n t r o l l e r

Channel2 Cr Output FIFO

Channel2 Cr Output FIFO

ChannelO Y or RGB Output FIFO

Master Control

CLK Divider

Data Packing

Slave Control

SAV/EAV Detect

T

7^

TF Serial to Parallel

2L2, Sensor Interface Signals

LJ

7

Pixel Data Bus

Figure 1. Interface for image and video capture. See: Multimedia System-on-a-Chip

References 1. Interfaces for Digital Component Video Signals in 525-Line and 625-Line Television Systems operating at the 4:2:2 Level of Recommendation ITU-R BT.601 (Part A), ITUR Recommendation BT.656-3,1995.

IMAGE AND VIDEO QUALITY ASSESSMENT Kalpana Seshadrinathan and Alan C. Bovik The University of Texas at Austin, USA Definition: Image and video quality assessment deals with quantifying the quality of an image or video signal as seen by a human observer using an objective measure.

Encyclopedia of Multimedia

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Introduction In this article, we discuss methods to evaluate the quality of digital images and videos, where the final image is intended to be viewed by the human eye. The quality of an image that is meant for human consumption can be evaluated by showing it to a human observer and asking the subject to judge its quality on a pre-defined scale. This is known as subjective assessment and is currently the most common way to assess image and video quality. Clearly, this is also the most reliable method as we are interested in evaluating quality as seen by the human eye. However, to account for human variability in assessing quality and to have some statistical confidence in the score assigned by the subject, several subjects are required to view the same image. The final score for a particular image can then be computed as a statistical average of the sample scores. Also, in such an experiment, the assessment is dependent on several factors such as the display device, distance of viewing, content of the image, whether or not the subject is a trained observer who is familiar with processing of images etc. Thus, a change in viewing conditions would entail repeating the experiment! Imagine this process being repeated for every image that is encountered and it becomes clear why subjective studies are cumbersome and expensive. It would hence be extremely valuable to formulate some objective measure that can predict the quality of an image. The problem of image and video quality assessment is to quantify the quality of an image or video signal as seen by a human observer using an objective measure. The quality assessment techniques that we present in this article are known as full-reference techniques, i.e. it is assumed that in addition to the test image whose quality we wish to evaluate, a "perfect" reference image is also available. We are, thus, actually evaluating the fidelity of the image, rather than the quality. Evaluating the quality of an image without a reference image is a much