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ALTER Procedure prInvSum_Generate -- Generate denormalized table that will speed-up the querying. As set nocount on Declare @intErrorCode int, @intTransactionCountOnEntry int Select @intErrorCode = @@Error If @intErrorCode = 0 Begin create table #Inv(ID int identity(1,1), Inventoryid int, Make varchar(50), Model varchar(50), Location varchar(50), Status varchar(15), FirstName varchar(30), LastName varchar(30), AcquisitionType varchar(12), Address varchar(50), City varchar(50), ProvinceId char(3), Country varchar(50), EqType varchar(50), Phone varchar(20), Fax varchar(20), Email varchar(128), UserName varchar(50), MakeModelSIdx int, LFNameSIdx int, CountrySIdx int) Select @intErrorCode = @@Error End If @intErrorCode = 0 Begin insert into #Inv(Inventoryid , Make , Model , Location , FirstName , LastName , AcquisitionType, Address , City ,ProvinceId , Country , EqType ,Phone , Fax , Email , UserName ) SELECT Inventory.Inventoryid, Equipment.Make, Equipment.Model, Location.Location, Contact.FirstName,



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code word assignment, the actual bit rate compression is achieved The quantization process is the source of virtually all the loss of information in the compression algorithm This is an important point, as it simplifies the design process and facilitates fine-tuning of the system The degree of subjective picture degradation caused by coefficient quantization tends to depend on the nature of the scenery being coded Within a given picture, distortions of some regions may be less apparent than in others The video coding system allows for the level of quantization to be adjusted for each macroblock in order to save bits, where possible, through coarse quantization Perceptual Weighting The human visual system is not uniformly sensitive to coefficient quantization error [10] Perceptual weighting of each source of coefficient quantization error is used to increase quantization coarseness, thereby lowering the bit rate.





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If you want to brew tea in a solar cooker, you can t expect to get boiling water and then make your tea conventionally instead take a jar and a couple of tea bags, put the tea bags in the jar along with some clean water (which you might have even got from your solar distilling apparatus!).

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The amount of visible distortion resulting from quantization error for a given coefficient depends on the coefficient number, or frequency, the local brightness in the original image, and the duration of the temporal characteristic of the error The dc coefficient error results in mean value distortion for the corresponding block of pixels, which can expose block boundaries This is more visible than higher-frequency coefficient error, which appears as noise or texture Displays and the HVS exhibit nonuniform sensitivity to detail as a function of local average brightness Loss of detail in dark areas of the picture is not as visible as it is in the brighter areas Another opportunity for bit savings is presented in textured areas of the picture, where highfrequency coefficient error is much less visible than in relatively flat areas Brightness and texture weighting require analysis of the original image because these areas may be well predicted.

Additionally, limiting its duration to one or two frames can easily mask distortion This effect is most profitably used after scene changes, where the first frame or two can be greatly distorted at normal speed without perceptible artifacts When transform coefficients are being quantized, the differing levels of perceptual importance of the various coefficients can be exploited by allocating the bits to shape the quantization noise into the perceptually less important areas This can be accomplished by varying the relative step sizes of the quantizers for the different coefficients The perceptually important coefficients maybe quantized with a finer step size than the others For example, low spatial frequency coefficients may be quantized finely and the less important high frequency coefficients may be quantized more coarsely A simple method to achieve different step.

Contact.LastName, AcquisitionType.AcquisitionType, Location.Address, Location.City, Location.ProvinceId, Location.Country, EqType.EqType, Contact.Phone, Contact.Fax, Contact.Email, Contact.UserName FROM EqType RIGHT OUTER JOIN Equipment ON EqType.EqTypeId = Equipment.EqTypeId RIGHT OUTER JOIN Inventory ON Equipment.EquipmentId = Inventory.EquipmentId LEFT OUTER JOIN AcquisitionType ON Inventory.AcquisitionTypeID = AcquisitionType.AcquisitionTypeId LEFT OUTER JOIN Location ON Inventory.LocationId = Location.LocationId LEFT OUTER JOIN Contact ON Inventory.OwnerId = Contact.ContactId order by Location, LastName, FirstName Select @intErrorCode = @@Error End -- now let s do record sorting ---- Make, Model ------------------If @intErrorCode = 0 Begin create table #tmp (SID int identity(1,1), ID int) Select @intErrorCode = @@Error End If @intErrorCode = 0 Begin insert into #tmp(ID) select ID from #inv order by Make, Model Select @intErrorCode = @@Error End If @intErrorCode = 0 Begin update #inv set MakeModelSIdx = #tmp.SId from #inv inner join #tmp on #inv.ID = #tmp.id Select @intErrorCode = @@Error End If @intErrorCode = 0 Begin drop table #tmp

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