NTIA Technical Memorandum TM-05-417 

Video Scaling Estimation Technique 

Margaret H. Pinson 

Stephen Wolf 

NTIA Technical Memorandum TM-05-417 

Video Scaling Estimation Technique 

Margaret H. Pinson 

Stephen Wolf 

U.S. DEPARTMENT OF COMMERCE 

Donald L. Evans, Secretary 

Michael D. Gallagher, Assistant Secretary 

for Communications and Information 

January 2005 

CONTENTS

Page 

ABSTRACT..................................................................................................................................................1 

1. INTRODUCTION................................................................................................................................1

2. PROBLEM SPECIFICATION ............................................................................................................2

3. ALGORITHM DESCRIPTION...........................................................................................................3

4. RESULTS ............................................................................................................................................8

5. CONCLUSION..................................................................................................................................15

6. REFERENCES...................................................................................................................................15

iii

VIDEO SCALING ESTIMATION TECHNIQUE 

Margaret H. Pinson and Stephen Wolf1

Digital video compression algorithms are being deployed that spatially stretch or 

shrink the video picture. Although small changes in spatial scaling are not usually 

noticeable to viewers, objective video quality measurement systems may be 

adversely impacted if the spatial scaling is not corrected. This report describes an

algorithm that can be used to automatically measure the amount of spatial scaling 

present in a video system. This algorithm obtains satisfactory computational 

complexity by (1) separating the searches for horizontal & vertical scaling factors,

(2) using image profiles rather than full images, and (3) using random rather than 

exhaustive searching techniques. 

Key words: calibration; objective; random search; spatial scaling; video quality 

1. INTRODUCTION 

Digital video compression algorithms are being deployed that do not preserve the spatial

dimensions, or scaling, of the input video picture. For instance the picture may be stretched or 

shrunk in the horizontal direction. There are several possible reasons for the presence of spatial 

scaling in today’s digital video systems. Video compression designers may be trying to preserve 

bits by shrinking the image size slightly, the video system may be designed for display on 

computer monitors where preserving image size is not an issue, or there may be errors in the 

spatial sampling used for the video system. Whatever the case, small changes in spatial scaling 

are not usually noticeable to viewers or if they are noticed, viewers may feel that the spatial 

scaling has little impact on quality. However, objective video quality measurement systems may 

be adversely impacted if the spatial scaling is not corrected before the quality measurements are 

performed. For instance, even a small uncorrected spatial scaling of several percent will cause a

common objective measurement such as peak signal to noise ratio (PSNR) to show a large 

impairment. 

This report describes an algorithm that can be used to automatically measure the amount of 

spatial scaling that is present in a video system. This algorithm is used in conjunction with 

algorithms that are designed to measure spatial registration (i.e., spatial shift) and temporal 

registration (i.e., temporal shift) since these calibration problems commonly coexist with video 

systems that perform spatial scaling. Every effort has been made to make the composite search 

algorithm computationally efficient. This report presents the algorithm in sufficient detail for

 

1

 The authors are with the Institute for Telecommunication Sciences, National Telecommunications and Information

Administration, U.S. Department of Commerce, 325 Broadway, Boulder, CO 80305. 
implementation by an automated measurement system. Results are also presented that give the 

performance of the algorithm for video streams that have digital video impairments. 

2. PROBLEM SPECIFICATION 

The primary goal of the algorithm is to find the amount of vertical and horizontal scaling that is 

present in a processed (i.e., output) video stream when compared to an original (i.e., input) video 

stream. However, other calibration problems that are present in the processed video stream (e.g., 

spatial and temporal registration) complicate the estimation of spatial scaling. Thus, for typical 

video systems, finding the amount of spatial scaling in a processed video stream involves at least 

five interrelated estimation problems: 

• Temporal registration – Estimating the temporal shift of the processed video stream with 

respect to the original video stream. 

• Horizontal scaling – Estimating the stretch or shrinkage in the horizontal direction of the 

processed video picture with respect to the original video picture. 

• Horizontal shift – Estimating the shift in the horizontal direction of the processed video 

picture with respect to the original video picture.

• Vertical scaling – Estimating the stretch or shrinkage in the vertical direction of the 

processed video picture with respect to the original video picture. 

• Vertical shift – Estimating the shift in the vertical direction of the processed video picture 

with respect to the original video picture. 

There are other potential calibration problems with the processed video stream (e.g., luminance 

gain, luminance level offset) that may affect the estimation of the above five quantities. 

However, to adequately address all unknown quantities simultaneously would result in a 

prohibitively slow search. Therefore, reasonable calibration values will be selected for these 

other unknown quantities. These assumptions will limit the scope of the search to the 

aforementioned five dimensions. 

However, even an exhaustive five dimension search would require a prohibitive amount of 

memory and time on today’s computers. A 10 second video sequence (a standard length scene 

for video quality testing purposes) of 525-line / NTSC video stored uncompressed in double 

precision (a common precision of computing) requires 840 MB of memory for just the luminance 

(Y) image plane. Consider two such video sequences (original and processed) and the need to

search over several seconds of temporal shift, perhaps ±20 pixels of spatial shift, and many 

combinations of spatial scaling to determine the optimal “alignment” of the processed sequence

with the original sequence. Computers would need to improve by several orders of magnitude in 

CPU speed unless the size of the search is significantly reduced. 

This algorithm uses several approximations to reduce the search space. The search is split into

two independent searches. One search seeks the horizontal scaling; a second search seeks the 

vertical scaling. Each two-dimensional video image is transformed into two one-dimensional

arrays by computing profiles of the image. A horizontal image profile for horizontal scaling 

estimation is computed by averaging each column of image pixels, and a vertical image profile 

for vertical scaling estimation is computed by averaging each row of image pixels. This reduces 

2 
the order of magnitude of the search from O(n5

) to O(n3) (spatial shift, spatial scale, temporal 

shift). A randomized search is performed over the remaining three dimensions instead of an 

exhaustive search to further speed computations. 

A deterministic non-exhaustive search could also be used to speed computations. This would 

involve designing a deterministic heuristic (i.e., a simple rule or educated guess) to guide the

search. However, randomized algorithms are preferable to deterministic algorithms when it is 

difficult to specify a heuristic that will guarantee good behavior. Randomization does not 

improve the worst case run-speed. However, heuristic algorithms will exhibit poor behavior

when given certain inputs, whereas randomized algorithms will only exhibit poor behavior from 

an unfortunate series of pseudo-random numbers. Randomized algorithms are particularly 

valuable in situations like this search where the advantages of good choices are more important 

than the disadvantages of bad choices. 

3. ALGORITHM DESCRIPTION 

The same core algorithm is used to independently estimate the horizontal and vertical scaling.

This section will present that core algorithm in terms of the horizontal scaling estimation. 

3.1. Horizontal Scaling Search 

NTSC (525-line) and PAL (625-line) video sampled according to ITU-R Recommendation 

BT.601 (henceforth abbreviated Rec. 601) may have a border of pixels and lines that do not 

contain a valid picture. The original video from the camera may only fill a portion of the Rec. 

601 frame. Some digital video compression schemes further reduce the area of the picture in 

order to save transmission bits. To prevent non-picture areas from influencing the spatial scaling

algorithm, they must be excluded. 

Table 1 gives reasonable default values for the border of invalid pixels around the edge of 

common image sizes. Pixels in this invalid region will be discarded by the search algorithm. 

Images in common intermediate format (CIF), source input format (SIF), and quarter resolution 

versions of these (QCIF and QSIF) typically do not have an invalid border, so no pixels are 

discarded. 

Table 1. Default Invalid Border for Common Video Sizes 

Video Type Rows Columns Invalid 

Top 

Invalid 

Left 

Invalid 

Bottom

Invalid 

Right 

NTSC (525-line) 486 720 20 24 18 24

PAL (625-line) 576 720 16 24 16 24

3 
 

Let Yn be the n

th luminance image in a video sequence containing N images. For interlace video, 

Yn is the n

th of N fields; for progressive video, Yn is the nth of N frames. Let Yn(v,h) be the 

coordinates of a pixel, where v is the vertical row index and h is the horizontal column index, and 

the upper-left coordinate of the image is v = 1, h = 1. Compute the horizontal profile of each 

image (i.e., average each column) and join the profiles together into a single profile array, P(h,n). 

( ) ∑ ( =

=

v

Yn v h

C

P h n

1

, 1 , )

C

 

, (1) 

where C is the total number of rows in each column of the image after eliminating the invalid 

border shown in Table 1. Apply (1) to the original video sequence to create Po(h,n); and to the 

processed video sequence to create Pp(h,n). For simplicity, we will assume that the original and 

processed video sequences both contain N images in time. 

We will perform a three dimensional search for horizontal scaling, horizontal shift, and temporal 

shift by comparing Po with Pp. Adjusting horizontal shift and time shift requires simple shifts of

Pp with respect to Po. Adjusting horizontal scaling requires profiles in Pp to be stretched or

shrunk. Let us define the function resample that is used to perform this spatial scaling, or 

resampling: 

Pr = resample(P,r), (2)

where r is the amount by which all profiles in P should be scaled. Here, r is an integer denoting

the amount of scaling such that r/10002

 is the multiplication factor by which each profile is

scaled or resampled. The function resample resamples each profile in P separately. The 

function resample applies an anti-aliasing (lowpass) FIR filter, assuming zero samples before 

and after the ends of the array to be resampled, and retains the center portion of the filtered array. 

Thus, the array returned, Pr, is of the same dimensions as the input array. The FIR filter used 

before resampling was designed by minimizing the weighted mean squared error between the

ideal brick wall lowpass filter and the actual filter. The weighting function comes from a 10 

point Kaiser window with a beta of 5. 

Notice that some samples at the top and bottom of Pr will now become invalid if function

resample shrinks the profiles (i.e., r is less than 1000). When profiles in Pr are shifted vertically 

(i.e., to find the horizontal shift), even more pixels at the top and bottom of Pr will become

invalid. The maximum number of invalid pixels, I, in each column can be found using (3). 

I = maxsearch_hs + ceiling(C * maxsearch_r/1000), (3) 

2

 Factors larger than 1000 may be used for more precision in the scaling calculation.

4 
where maxsearch_hs is the maximum horizontal shift to be searched; function ceiling rounds a 

value up to the nearest integer; and maxsearch_r is a constant corresponding to the maximum 

difference in scaling to be considered, expressed as a deviation from 1000. For example,

maxsearch_r = 50 would indicate r varying from 950 to 1050, which corresponds to searching 

scaling factors from 95% to 105%. 

Each combination of horizontal scaling, horizontal shift, and temporal shift must be evaluated 

separately. The evaluation criteria calculation takes four steps. First, apply the horizontal 

scaling to Pp, 

Pp,r = resample(Pp, r). (4)

Second, take a difference between the original profile array, Po, and the scaled processed profile 

image, Pp,r, after adjusting for horizontal shift (hs) and temporal shift (ns), 

D(h,n) = Po(h,n) - Pp,r(h+hs, n+ns). (5) 

Third, take the standard deviation over each column of the array D(h,n), excluding samples 

within I of the top or bottom of each column (i.e., because these samples might be invalid), 

T(n) = stdev ( D(h,n) ), for h = I+1 to C-I. (6) 

Here, n ranges from (1+maxsearch_ns) to (N-maxsearch_ns) rather than from 1 to N, where 

maxsearch_ns is the maximum temporal shift that will be examined in the search. We define the 

optimal alignment point for some horizontal scaling r, horizontal shift hs, and temporal shift ns to 

be the point where the standard deviation of the difference between the original and processed

profiles is minimized. However, due to the nature of digital video systems (e.g., some of which 

drop video frames, repeat video frames, present video frames with errors etc.), not all processed

video frames will align with original video frames for one temporal shift ns between the

processed and original sequences. Therefore, a function is required to discard many of the 

processed frames that are not temporally aligned at temporal shift ns. This function is 

represented as, 

V = below25%(T(n)), (7)

where below25% sorts the values in array T(n) from low to high, and computes the average of all 

values that are less than or equal to the 25th percentile. The net effect of this function is to 

discard the worst 75% of the matched processed and original image pairs and only consider the 

25% best matched pairs. 

V in (7) is a function of horizontal scaling (r), horizontal shift (hs), and temporal shift (ns). The

horizontal scaling, horizontal shift, and temporal shift that minimize V from (7) will be used as 

the estimates of the actual values for the processed video sequence. However, an exhaustive 

search over those three dimensions would be prohibitively time consuming. Therefore, a 

randomized search strategy is used instead. 

The strategy contains two stages. The first stage searches randomly and uniformly across the 

entire search space. The second stage refines the results of the first stage. It uses a 3-

5 
dimensional Gaussian distribution to focus the search in the vicinity of the current best point in

space. Each time a new best point is identified, the search is recentered about that point. 

Let us define five variables: W, min_W, min_hs, min_r, and min_ns. W(r,hs,ns) will hold V for 

each horizontal scale r, horizontal shift hs, and temporal shift ns. Initialize W(r,hs,ns) to NaN 

(Not-A-Number). min_W will hold the minimum V, whose value will be associated with

horizontal scale min_r, horizontal shift min_hs, and temporal shift min_ns. Initialize min_W to

infinity. Note that r will range from (1000 – maxsearch_r) to (1000 + maxsearch_r), hs will 

range from -maxsearch_hs to +maxsearch_hs, and ns will range from –maxsearch_ns to 

+maxsearch_ns. Finally, let us choose TRIES, the number of evaluations to be performed before

the algorithm declares that a solution has been found. A default value of TRIES = 3000 seems to 

work well and is the recommended setting. 

For a number of evaluations equal to TRIES / 5, choose values for r, hs, and ns randomly over the

range to be searched, using a uniform distribution of random values. 

r = round (1000 - maxsearch_r - 0.5 + ((maxsearch_r * 2 + 1) * rand)), (8) 

hs = round ( -maxsearch_hs - 0.5 + ((maxsearch_hs * 2 + 1) * rand)), (9) 

ns = round ( -maxsearch_ns - 0.5 + ((maxsearch_ns * 2 + 1) * rand)), (10) 

where rand is a random number generator that yields numbers from the uniform distribution over 

the range (0, 1). 

For each randomly chosen coordinate (r,hs,ns), compute V as shown in (7) which will give the 

value for W(r,hs,ns). Update the values of W, min_W, min_hs, min_r, and min_ns as shown in 

(11) and (12). 

W(r,hs,ns) = V (11) 

 If V < min_W, then min_W = V, min_r = r, min_hs = hs, and min_ns = ns. (12) 

If a coordinate (r,hs,ns) is chosen twice, the calculation of V is skipped. Duplicate coordinates 

are detected by testing whether W(r,hs,ns) contains NaN. Duplicate coordinates are counted in 

the number of evaluations to be tried. 

After TRIES / 5 iterations, the coordinate (min_r, min_hs, min_ns) will be a fairly close estimate

of the actual coordinate. Perform an additional TRIES * 4 / 5 iterations as shown above but with 

a modified distribution of random values. The new random distribution increases the likelihood 

of the chosen coordinate being closer to the current best point in the search space. 

r = min_r + round ( 6 * rand_norm) (13) 

hs = min_hs + round ( 2 * rand_norm) (14) 

ns = min_ns + round ( 2 * rand_norm) (15) 

6 
In (13)-(15), rand_norm is a random number generator that yields a normal distribution with 

zero mean and unity variance. If the coordinate (r,hs,ns) is outside the range to be searched, then

another random coordinate is chosen instead. The long tails of the normal distribution help

prevent the algorithm from locking in on a local minimum rather than the global minimum. The

quick handling of duplicate coordinates allows TRIES to be set to a large number without 

negatively impacting run speed. Note that (13)-(15) continually recenter the search about the 

current best point in the search space. 

After the specified number of iterations, the value min_r is returned as an estimate of the 

horizontal scaling. The values min_hs and min_ns will not be considered any further as more 

precise algorithms for estimating these calibration quantities (after spatial scaling is corrected) 

are already available and standardized [1] [2]. 

3.2. Vertical Scaling Search 

The vertical scaling search is conducted identically to the horizontal scaling search, except that 

(1) is changed to (16), to accommodate the change in scaling orientation.

( ) ∑ ( =

=

h

Yn v h

R

P v n

1

, 1 , )

R

 (16) 

where R is the total number of columns in each row of the image after eliminating the invalid 

border shown in Table 1. This creates the vertical profile of each image (i.e., average each row) 

and joins the profiles together into a single image, P(v,n). After the specified number of 

iterations, the value min_r is returned as an estimate of the vertical scaling. Thus, the searches 

for horizontal and vertical scaling are conducted separately. 

3.3. Error Resiliency 

Tests performed on a limited set of video clips indicated that the use of a randomized rather than

exhaustive search does not seem to have a significant impact on the algorithm’s estimate of 

spatial scaling. The randomized search from (13), (14), and (15) effectively conducts a localized 

exhaustive search, combined with a limited search for more distant scaling / shift / time 

possibilities. However, the averaging of columns or rows in (1) and (16) discards a significant 

amount of information from the image sequence. When combined with impairments in the video 

sequence, an incorrect spatial scaling estimate can result. This is not because the randomized 

search reaches a false minimum, but rather because the actual minimum of the profiled spatialtemporal image indicates an erroneous scaling. Therefore, the spatial scaling algorithm should 

ideally be applied to several different video sequences that have been passed through the same

video system. If the majority of these scaling results from several different sequences indicate 

one scaling number, then the user can be more confident that this answer is correct. If the spatial 

scaling results from different sequences are not identical, the user should compute the median 

result to select the final horizontal and vertical scaling numbers. 

7 
A visual inspection of the final scale-corrected images may be another good method of checking 

the spatial scaling results for a processed video sequence. However, an accurate visual 

inspection will require that the processed video sequence be fully calibrated with respect to 

spatial registration and temporal registration. Any errors in these calibration values will 

invalidate the visual inspection. If the video sequence in question contains repeated frames or 

dynamic time warping (i.e., time varying video delays), then obtaining two time-aligned frames

can be quite difficult. It is suggested that the viewer use a video sequence that is either still or

nearly still for this visual check. 

4. RESULTS 

Identical scaling results for multiple sequences indicate a high degree of confidence that the 

scaling results are accurate. Scaling results that vary widely indicate ambiguity. Most video

systems fall in between these two extremes and produce a single scaling factor for many of the 

sequences but adjacent scaling factors for some sequences, with errors distributed according to a 

normal distribution. Video systems that contain transmission errors or other severe impairments

can result in a wide, more uniform distribution of scaling factors for different sequences. 

This automated scaling estimation algorithm was checked by examining 2506 individual video 

clips processed through a variety of video transmission systems that do not appear to contain any 

spatial scaling (horizontal or vertical). This lack of scaling was checked visually by displaying 

the difference between the luminance planes of a fully calibrated processed image and the 

corresponding original image. These video clips were not used to train or develop the algorithm.

Figure 1 and Figure 2 depict the distribution of vertical and horizontal scaling estimates, 

respectively, calculated automatically for the 2506 individual video clips. Figure 3 shows the 

cumulative distribution function of the distance between individual clips’ scaling and 1000. 

When examining these figures, please recall that 1000 indicates “no scaling”. 85.28% of the 

individual clips’ vertical scaling estimates were within ±2 of 1000 (i.e., in the range [998,1002]); 

and 95.65% of the individual clips’ horizontal scaling estimates were within ±2 of 1000. 

Overall, 83.16% of these individual clips had both vertical and horizontal scaling estimates

within ±2 of 1000. 89.27% of individual clips’ vertical scaling estimates were within ±3 of 1000 

(i.e., in the range [997, 1003]); and 96.97% of individual clips’ horizontal scaling estimates were 

within ±3 of 1000. Overall, 87.79% of these individual clips had both vertical and horizontal 

scaling estimates within ±3 of 1000. 

8 
Figure 1. Histogram of vertical scaling results for 2506 un-scaled clips. 

Figure 2. Histogram of horizontal scaling results for 2506 un-scaled clips. 

9 
Figure 3. Cumulative distribution of the individual clips’ scaling. 

When results are filtered across scenes for each video system (i.e., the median of the individual 

clips’ vertical and horizontal scaling estimates, where each clip has been passed through the 

same video system), the accuracy of the algorithm increases. The aforementioned 2506 

individual video clips are associated with 290 video systems. Figure 4 and Figure 5 depict the 

distribution of these vertical and horizontal scaling estimates, respectively, calculated with 

median filtering on these 290 video systems. Figure 6 shows the cumulative distribution

function of the distance between systems’ scaling and 1000. Now, 88.54% of the vertical scaling 

estimates were within ±2 of 1000; and 98.42% of the horizontal scaling estimates were within ±2 

of 1000. Overall, 87.75% had both vertical and horizontal scaling estimates within ±2 of 1000. 

92.89% of the vertical scaling estimates were within ±3 of 1000; and 98.82% of the horizontal 

scaling estimates were within ±3 of 1000. Overall, 92.10% had both vertical and horizontal

scaling estimates within ±3 of 1000. These statistics show an overall improvement over the

individual clip statistics.

10 
Figure 4. Histogram of vertical scaling results with median filtering. 

Figure 5. Histogram of horizontal scaling results with median filtering.

11 
Figure 6. Cumulative distribution of video system’s scaling. 

Notice that a significant number of the video systems represented in Figure 5 had results that

indicated horizontal scalings of 998, 999, 1001, and 1002. The 998 and 1002 scalings indicate a 

1.44 pixel stretching or shrinking across a 720 pixel wide image. These horizontal scalings are 

often too small to be reliably detected via manual examination when digital video system 

impairments (such as blurring or encoding artifacts) are present in the processed video stream. 

Because these small scaling factors cannot be easily verified, the user is advised to consider 

scaling factors that are within plus or minus 3 of 1000 to be indicative of a video system that

does not spatially scale images. 

The performance of the scaling algorithm was also analyzed using video clips passed through 

seven transmission systems that exhibited known video scaling. Some of these clips were used 

to train or develop the algorithm. Figure 7 contains histograms for the clips passed through the 

three video systems that contained vertical scalings. System 5 was a 22 kbits/s video 

transmission system that contained serious impairments. These serious impairments caused the 

scaling algorithm to produce unreliable results for three of the six video sequences. System 6 

depicts a tight, reliable grouping. Some clips indicated a vertical scaling of 1011 and others 

1013, where the majority of clips indicated the actual vertical scaling of 1012 (confirmed using 

visual examination). This tight spread of scalings around the correct answer is the most common 

error distribution for systems that have small amounts of impairments, whereas the error 

distribution shown for system 7 is more typical of low quality video systems. 

12 
Figure 7. Histograms of typical vertical scaling results. 

Figure 8 contains histograms for the clips passed through the seven video systems, all of which 

contained horizontal scalings. These seven histograms show a range of responses of the scaling 

algorithm when applied to different video systems. System 3 used CIF images (352 columns by 

288 rows). The small image size and high levels of impairments contributed to the increased 

variability of results from individual clips. System 4 was a video system that was tested both 

with and without transmission errors. Clips containing serious transmission error impairments

are responsible for the unreliable spread of vertical scalings. Notice that all 13 scenes used to

analyze system 6 indicated an exact horizontal stretch of 1002. Although the proximity of this 

scaling to 1000 may tend to indicate no scaling, the conclusive presence of a vertical scaling 

factor of 1012 (see Figure 7), combined with all scenes being in perfect agreement on the 1002 

horizontal scaling, is indicative of an actual horizontal scaling factor of 1002. Visual

examination of these scenes agreed with the scaling numbers produced by the automated

algorithm.

13 
Figure 8. Histograms of typical horizontal scaling results. 

14 
5. CONCLUSION 

We have presented an automated algorithm for estimating the spatial scaling introduced by video 

transmission systems. This algorithm obtains satisfactory computational complexity by (1) 

separating the searches for horizontal & vertical scaling factors, (2) using image profiles rather 

than full images, and (3) using random rather than exhaustive searching techniques. 

This automated algorithm obtains reasonable reliability when the results from multiple video 

clips are jointly analyzed. Although some combinations of scenes and video impairments

produce erroneous results, the use of multiple clips mitigates the impact of these errors on the

overall scaling factors that the algorithm produces. However, the scaling estimation algorithm is 

not sufficiently robust to be recommended as a fully automated solution. The horizontal and 

vertical image profiling process that was necessary for efficient computations may discard too

much information. Thus, a visual verification as to the correctness of the scaling factors 

produced by the algorithm is advised. The user is also advised to consider scaling factors that 

are within ±3 of 1000 to be indicative of a video system that does not spatially scale images. 

6. REFERENCES 

[1] S. Wolf and M. Pinson, “Video quality measurement techniques,” NTIA Report 02-392, June

2002. Available: www.its.bldrdoc.gov/n3/video/documents.htm

[2] ANSI T1.801.03 – 2003, “American National Standard for Telecommunications – Digital 

transport of one-way video signals – Parameters for objective performance assessment,” 

American National Standards Institute. 

15 
FORM NTIA-29 U.S. DEPARTMENT OF COMMERCE

(4-80) NAT’L. TELECOMMUNICATIONS AND INFORMATION ADMINISTRATION 

BIBLIOGRAPHIC DATA SHEET 

1. PUBLICATION NO. 

TM-05-417 

2. Government Accession No. 3. Recipient’s Accession No. 

5. Publication Date 

January 2005

4. TITLE AND SUBTITLE

Video Scaling Estimation Technique 6. Performing Organization

Code 

7. AUTHOR(S) 

Margaret H Pinson and Stephen Wolf

9. Project/Task/Work Unit No. 

3141011-300

8. PERFORMING ORGANIZATION NAME AND ADDRESS 

Institute for Telecommunication Sciences 

National Telecommunications & Information Administration 

U.S. Department of Commerce 

325 Broadway

Boulder, CO 80305

10. Contract/Grant No. 

11. Sponsoring Organization Name and Address 

National Telecommunications & Information Administration 

Herbert C. Hoover Building

14th & Constitution Ave., NW 

Washington, DC 20230

12. Type of Report and Period

Covered 

14. SUPPLEMENTARY NOTES 

15. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a

significant bibliography or literature survey, mention it here.) 

Digital video compression algorithms are being deployed that spatially stretch or shrink the video picture. 

Although small changes in spatial scaling are not usually noticeable to viewers, objective video quality 

measurement systems may be adversely impacted if the spatial scaling is not corrected. This report 

describes an algorithm that can be used to automatically measure the amount of spatial scaling present in 

a video system. This algorithm obtains satisfactory computational complexity by (1) separating the 

searches for horizontal & vertical scaling factors, (2) using image profiles rather than full images, and (3) 

using random rather than exhaustive searching techniques. 

16. Key Words (Alphabetical order, separated by semicolons) 

calibration; objective; random search; spatial scaling; video quality 

18. Security Class. (This report) 

Unclassified 

20. Number of pages 

 17 

17. AVAILABILITY STATEMENT 

 F UNLIMITED. 

19. Security Class. (This page) 

Unclassified 

21. Price: 
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