By A. Hanjalic
This e-book offers an in-depth remedy of the 3 very important themes on the topic of picture and video databases: recovery, watermarking and retrieval. it's the results of the participation of the Delft collage of know-how within the eu Union ACTS application, a pre-competitive R&D application on complicated Communications applied sciences and companies (1994-1998). particularly the ebook has benefited from participation within the AURORA and damage initiatives respectively automatic movie and video recovery and garage for multimedia platforms (watermarking & retrieval).
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3. The a m „ (t) and b m , n (t) are not very reliable in a number of cases. The first case is that of uniform image intensities. For any original image intensity in a uniform region, there are infinite combinations of a(i) and b(i) that lead to the same observed intensity. 16) discard the noise in y(i) originating from h(i). This leads to values for a m , n (t) that are too small. Considerable errors result in regions Wm,n in which the signal variance is smaller than the noise variance. The signal-to-noise ratio, defined as var(y) / var(h), determines the variance of the errors in the estimated model parameters.
The positive effects of reducing intensity flicker prior to compression are shown by [Ric85], [Roo]. The analysis given here shows that artifacts have a negative influence on the coding efficiency of MPEG2. Therefore removing artifacts prior to encoding is beneficial. It is difficult to quantify the benefits beforehand because they depend strongly on the nature of the unimpaired signal, the strength of the impairments, and the effectiveness of the restoration algorithms. It should be noted that not all impairments decrease the coding efficiency.
Because these estimates will never be perfect, the effects of errors in a ( i ) and b(i) on y(t) is investigated. To simplify the analysis, the influence of noise is discarded. For ease of notation, the following analysis leaves out the spatial and temporal indices. Let a = a + Da and b - b + Db. 1 plots the reconstruction error as a function of Da and Db with a = 1, b = 0 and y=100. 7) shows that Ay is much more sensitive to errors in a ( i ) than to errors in b ( i ) . It also shows that the sensitivity due to errors Act can be minimized in absolute terms by centering the range of image intensities around 0.