What is the definition of "Error surface" in HEVC - hevc

I'm reading a paper about sub-pixel motion estimation optimization algorithm in HEVC;
while all the proposed concepts are based on "Modeling the Error Surface" in the search range(search window)during algorithm;
Does anybody by any chance know the definition of "Error Surface" here?
And what I'm lookin for is definitely not this: Freeform surface modelling.
Thanks.
By the way, the paper's link is here.

The picture below (from the Geneva meeting in January this year) shows the
Integer samples (shaded blocks with upper-case letters) and fractional sample positions (un-shaded blocks with lower-case letters) for quarter sample luma interpolation
The following calculations are needed for the quarter-sample-interpolation:
a0,0 = ( −A−3,0 + 4 * A−2,0 − 10 * A−1,0 + 58 * A0,0 +  17 * A1,0 − 5 * A2,0 + A3,0 )  >>  shift1 (8‑292)
b0,0 = ( −A−3,0 + 4 * A−2,0 − 11 * A−1,0 + 40 * A0,0 +  40 * A1,0 − 11 * A2,0 + 4 * A3,0 − A4,0 )  >>  shift1 (8‑293)
c0,0 = ( A−2,0 − 5 * A−1,0 + 17 * A0,0 +  58 * A1,0 − 10 * A2,0 + 4 * A3,0 − A4,0 )  >>  shift1 (8‑294)
d0,0 = ( −A0,−3 + 4 * A0,−2 − 10 * A0,−1 + 58 * A0,0 +  17 * A0,1 − 5 * A0,2 + A0,3 )  >>  shift1 (8‑295)
h0,0 = ( −A0,−3 + 4 * A0,−2 − 11 * A0,−1 + 40 * A0,0 +  40 * A0,1 − 11 * A0,2 + 4 * A0,3 − A0,4 )  >>  shift1 (8‑296)
n0,0 = ( A0,−2 − 5 * A0,−1 + 17 * A0,0 +  58 * A0,1 − 10 * A0,2 + 4 * A0,3 − A0,4 )  >>  shift1 (8‑297)
– The samples labelled e0,0, i0,0, p0,0, f0,0, j0,0, q0,0, g0,0, k0,0, and r0,0
are derived by applying an 8-tap filter to the samples a0,i, b0,i and c0,i with
i = −3..4 in the vertical direction as follows:
e0,0 = ( −a0,−3 + 4 * a0,−2 − 10 * a0,−1 + 58 * a0,0 +  17 * a0,1 − 5 * a0,2 + a0,3 )  >>  shift2 (8‑298)
i0,0 = ( −a0,−3 + 4 * a0,−2 − 11 * a0,−1 + 40 * a0,0 +  40 * a0,1 − 11 * a0,2 + 4 * a0,3 − a0,4 )  >>  shift2 (8‑299)
p0,0 = ( a0,−2 − 5 * a0,−1 + 17 * a0,0 +  58 * a0,1 − 10 * a0,2 + 4 * a0,3 − a0,4 )  >>  shift2 (8‑300)
f0,0 = ( −b0,−3 + 4 * b0,−2 − 10 * b0,−1 + 58 * b0,0 +  17 * b0,1 − 5 * b0,2 + b0,3 )  >>  shift2 (8‑301)
j0,0 = ( −b0,−3 + 4 * b0,−2 − 11 * b0,−1 + 40 * b0,0 +  40 * b0,1 − 11 * b0,2 + 4 * b0,3 − b0,4 )  >>  shift2 (8‑302)
q0,0 = ( b0,−2 − 5 * b0,−1 + 17 * b0,0 +  58 * b0,1 − 10 * b0,2 + 4 * b0,3 − b0,4 )  >>  shift2 (8‑303)
g0,0 = ( −c0,−3 + 4 * c0,−2 − 10 * c0,−1 + 58 * c0,0 +  17 * c0,1 − 5 * c0,2 + c0,3 )  >>  shift2 (8‑304)
k0,0 = ( −c0,−3 + 4 * c0,−2 − 11 * c0,−1 + 40 * c0,0 +  40 * c0,1 − 11 * c0,2 + 4 * c0,3 − c0,4 )  >>  shift2 (8‑305)
r0,0 = ( c0,−2 − 5 * c0,−1 + 17 * c0,0 +  58 * c0,1 − 10 * c0,2 + 4 * c0,3 − c0,4 )  >>  shift2 (8‑306)
Quite a mouthful as you can see...
The paper you are referring to error surface is probably the difference between the pixel-values calculated using the method proposed in the standard and the second-order-function proposed in the paper. Hope it helps :-)

Related

A value is trying to be set on a copy of a slice from a DataFrame - don't understand ist

I know this topic has been discussed a lot and I am so sorry, that I stil dont't find the sulution, even the difference between a view and a copy is easy to understand (in other languages)
def hole_aktienkurse_und_berechne_hist_einstandspreis(index, start_date, end_date):
df_history = pdr.get_data_yahoo(symbols=index, start=start_date, end=end_date)
df_history['HistEK'] = df_history['Adj Close']
df_only_trd_index = df_group_trade.loc[index].copy()
for i_hst, r_hst in df_history.iterrows():
df_bis = df_only_trd_index[(df_only_trd_index['DateClose']<=i_hst) & (df_only_trd_index['OpenPos']==0)].copy()
# here comes the part what causes the trouble:
df_history.loc[i_hst]['HistEK'] = df_history.loc[i_hst]['Adj Close'] - df_bis['Total'].sum()/100.0
return df_history
I think I tried nearly everithing, but I don't get it. python is not easy when it comes to this topic.
When you have to specify bow index and column in .loc you have to put all together otherwise the annoying message relative to views appears.
df_history.loc[i_hst, 'HistEK'] = df_history.loc[i_hst, 'Adj Close'] - df_bis['Total'].sum()/100.0
Look the examples here

Sentinel 1 data gaps in swath overlap (not sequential scenes) in Google Earth Engine

I am working on a project using the Sentinel 1 GRD product in Google Earth Engine and I have found a couple examples of missing data, apparently in swath overlaps in the descending orbit. This is not the issue discussed here and explained on the GEE developers forum. It is a much larger gap and does not appear to be the product of the terrain correction as explained for that other issue.
This gap seems to persist regardless of year changes in the date range or polarization. The gap is resolved by changing the orbit filter param from 'DESCENDING' to 'ASCENDING', presumably because of the different swaths or by increasing the date range. I get that increasing the date range increases revisits and thus coverage but is this then just a byproduct of the orbital geometry? ie it takes more than the standard temporal repeat to image that area? I am just trying to understand where this data gap is coming from.
Code example:
var geometry = ee.Geometry.Polygon(
[[[-123.79472413785096, 46.20720039434629],
[-123.79472413785096, 42.40398120362418],
[-117.19194093472596, 42.40398120362418],
[-117.19194093472596, 46.20720039434629]]], null, false)
var filtered = ee.ImageCollection('COPERNICUS/S1_GRD').filterDate('2019-01-01','2019-04-30')
.filterBounds(geometry)
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
.filter(ee.Filter.eq('instrumentMode', 'IW'))
.select(["VV","VH"])
print(filtered)
var filtered_mean = filtered.mean()
print(filtered_mean)
Map.addLayer(filtered_mean.select('VH'),{min:-25,max:1},'filtered')
You can view an example here: https://code.earthengine.google.com/26556660c352fb25b98ac80667298959

Matplotlib - Draw H and V line by specifying X or Y value on a plot

I was wondering today about how finding a specific value on a plot and drawing the right line that goes with. I used to do that on an old chart library, and I was wondering that perhaps this functionnality exist but I don't know how to find it.
The result should look like this: https://miro.medium.com/max/1070/1*Ckhi9soE9Lx2lIf9tPVLMQ.png
To provide some context, I'm doing a PCA over my data, and I would like to point out some thresholds at 97.5, 99 and 99.5% of explained cumuled variance.
Have a great day!
EDIT:
See Answer
As solved by ImportanceOfBeingErnest, here is the code:
whole_pca = PCA().fit(np.array(inputs['Scale'].tolist()))
cumul = np.cumsum(np.round(whole_pca.explained_variance_ratio_, decimals=3)*100)
over_95 = np.argmax(cumul>95)
over_99 = np.argmax(cumul>99)
over_995 = np.argmax(cumul>99.5)
plt.plot(cumul)
plt.plot([0,over_95,over_95], [95,95,0])
plt.plot([0,over_99,over_99], [99,99,0])
plt.plot([0,over_995,over_995], [99.5,99.5,0])
plt.xlim(left=0)
plt.ylim(bottom=80)
plt.ylabel('% Variance Explained')
plt.xlabel('# of Features')
plt.title('PCA Analysis')
Result in:
Thank you!

How to use org.openimaj.ml.gmm to construct speaker models.

I would like to know how I can get GMM speaker model using OpenIMaj library.
org.openimaj.ml.gmm.GaussianMixtureModelEM. I have tried following
GaussianMixtureModelEM gmm = new GaussianMixtureModelEM
(DEFAULT_NUMBER_COMPONENTS,GaussianMixtureModelEM.CovarianceType.Diagonal);
MixtureOfGaussians mixture = gmm.estimate(data);
boolean convergerd = gmm.hasConverged();
I get true that GaussianMixtureModelEM has converged, I am lost where to go from here. Any help guidance would be appreciated.
Given your comment, then mixture.estimateLogProbability(point) should do what you want (see http://www.openimaj.org/apidocs/org/openimaj/math/statistics/distribution/MixtureOfGaussians.html#estimateLogProbability(double[])).

MATLAB: Resizing a figure properly

I have a figure that I would like to resize and afterwards print as a PDF.
Using something like
set(hFig, 'PaperUnits', 'centimeters')
set(hFig, 'PaperSize', [x_B x_H]);
works as long as I do not resize the figure too drastically. If I reduce the height then at some points the xlabel moves out of the figure. I have searched a lot but only found an solution to manually resize the underlying axes-object
scalefactor = 0.96;
movefactor = 0.82;
hAx = get(gcf,'CurrentAxes');
g = get(hAx,'Position');
% 1=left, 2=bottom, 3=width, 4=height
g(2) = g(2) + (1-movefactor)/2*g(4);
g(4) = scalefactor*g(4);
set(hAx,'Position',g);
I do not like this approach since I have to manually adjust the two factors.
Before printing I set the 'interpreter' to 'latex' of all text-objects (if that is of concern).
Printing is achieved using
print(hFig, '-dpdf', '-loose', 'test.pdf');
I hoped to loosen the bounding box by using '-loose'. Any help is highly appreciated!
edit:
It seems that really the interpreter (none, tex, latex) plays a role in this. I got inspired by this post here (http://stackoverflow.com/questions/5150802/how-to-save-plot-into-pdf-without-large-margin-around) and came up with this solution:
tightInset = get(gca, 'TightInset');
position(1) = tightInset(1);
position(3) = 1 - tightInset(1) - tightInset(3);
if strcmpi(x_Interpreter,'latex')
position(2) = tightInset(2)+ 1*tightInset(4);
position(4) = 1 - tightInset(2) - 2*tightInset(4);
else
position(2) = tightInset(2)+ 0*tightInset(4);
position(4) = 1 - tightInset(2) - 1*tightInset(4);
end
set(gca, 'Position', position);
This may not solve your problem completely (it may just help clean up your code), but I found the fig code in the file exchange to be helpful: it lets you easily set the exact size of figures without bordering white space.