CISPR TR 29:2020

CISPR TR 29 ®
Edition 2.0 2020-05
REDLINE VERSION
TECHNICAL
REPORT
colour
inside
INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE
Television broadcast receivers and associated equipment – Immunity
characteristics – Methods of objective picture assessment
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CISPR TR 29 ®
Edition 2.0 2020-05
REDLINE VERSION
TECHNICAL
REPORT
colour
inside
INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE

Television broadcast receivers and associated equipment – Immunity

characteristics – Methods of objective picture assessment

INTERNATIONAL
ELECTROTECHNICAL
COMMISSION
ICS 33.100.20 ISBN 978-2-8322-8345-5

– 2 – CISPR TR 29:2020 RLV © IEC:2020
CONTENTS
FOREWORD . 3
1 Scope . 5
2 Normative references . 5
3 Abbreviated terms . 5
4 Test method for objective picture assessment . 5
5 Methodology for detection of analogue picture degradations . 6
5.1 General . 6
5.2 Algorithm for superimposed patterns, moiré patterns . 6
5.3 Algorithm for loss of luminance and contrast . 7
5.4 Algorithm for loss of colour . 7
5.5 Algorithm for loss of synchronisation . 7
6 Methodology for detection of digital picture degradations . 8
6.1 General . 8
6.2 Algorithm for blocking . 8
6.3 Algorithm for frozen patterns, stop of moving element . 8
6.4 Algorithm for total loss of picture, irrecoverable data stream error . 8
7 Alternative methodology for detection of digital picture degradations . 9
7.1 Test pattern . 9
7.2 Analysis . 9
7.2.1 General . 9
7.2.2 Automatic spatial synchronisation . 10
7.2.3 Spatial activity parameter . 10
7.2.4 Temporal activity parameter . 10
7.2.5 Blocking effect parameter . 10
7.3 Comparison . 10
Bibliography . 12

Figure 1 – Colour bar pattern with test elements for detection of analog picture
degradation . 7
Figure 2 – Colour bar pattern with moving element for detection of digital picture
degradation . 8
Figure 3 – Alternative colour bar pattern with moving element for detection of digital
picture degradation . 9

INTERNATIONAL ELECTROTECHNICAL COMMISSION
____________
TELEVISION BROADCAST RECEIVERS AND ASSOCIATED EQUIPMENT –
IMMUNITY CHARACTERISTICS –
METHODS OF OBJECTIVE PICTURE ASSESSMENT

FOREWORD
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– 4 – CISPR TR 29:2020 RLV © IEC:2020
The main task of IEC technical committees is to prepare International Standards. However, a
technical committee may propose the publication of a Technical Report when it has collected
data of a different kind from that which is normally published as an International Standard, for
example "state of the art".
CISPR 29, which is a technical report, has been prepared by CISPR subcommittee I:
Electromagnetic compatibility of information technology equipment, multimedia equipment and
receivers.
This second edition cancels and replaces the first edition published in 2004. This edition
constitutes a technical revision.
This edition includes the following significant technical changes with respect to the previous
edition:
a) update of the references, and
b) editorial improvements.
The text of this Technical Report is based on the following documents:
Draft TR Report on voting
CIS/I/634/DTR CIS/I/638/RVDTR
Full information on the voting for the approval of this Technical Report can be found in the
report on voting indicated in the above table.
This document has been drafted in accordance with the ISO/IEC Directives, Part 2.
The committee has decided that the contents of this document will remain unchanged until the
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• reconfirmed,
• withdrawn,
• replaced by a revised edition, or
• amended.
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of its contents. Users should therefore print this document using a colour printer.

TELEVISION BROADCAST RECEIVERS AND ASSOCIATED EQUIPMENT –
IMMUNITY CHARACTERISTICS –
METHODS OF OBJECTIVE PICTURE ASSESSMENT

1 Scope
This document describes the algorithms used for objective picture assessment in immunity tests
of analogue and digital TV broadcast receivers and associated equipment.
The algorithms used were developed on the basis of the specifications originally included in
Annex K of CISPR 20:2002/AMD2:2004, the later edition of which has been replaced by
CISPR 35:2016. The method of objective picture assessment described in that annex employs
the same interference mechanism and is based on the same wanted signal definition as
specified in CISPR 20 CISPR 35 for subjective picture assessment. Objective picture
assessment, therefore, constitutes an alternative to the subjective method and offers the
advantage of direct correlation to the subjective method.
2 Normative references
The following referenced documents are indispensable for the application of this document. For
dated references, only the edition cited applies. For undated references, the latest edition of
the referenced document (including any amendments) applies.
CISPR 20:2002, Sound and television broadcast receivers and associated equipment –
Immunity characteristics – Limits and methods of measurement
Amendment 1 (2002)
ITU-R BT.500-10, Methodology for the subjective assessment of the quality of television
pictures
ITU-R BT.801-1, Test signals for digitally encoded colour television signals conforming with
Recommendations ITU-R BT.601 (Part A) and ITU.R BT.656
There are no normative references in this document.
3 Abbreviated terms
For the purposes of this document, the following abbreviated terms apply.
CCVS composite colour video signal (chrominance, video, blanking and sync signal)
DCT discrete cosine transform
EUT equipment under test
HSL hue, saturation, luminance (colour space model)
SSCQE (single stimulus continuous quality evaluation)
4 Test method for objective picture assessment
Objective picture assessment is based on comparison of the picture displayed on an EUT with
a reference picture or a reduced reference picture.

– 6 – CISPR TR 29:2020 RLV © IEC:2020
Both the reference picture and the test picture can be recorded from the EUT monitor by means
of a video camera or at the EUT's video output (CCVS) direct if it has one.
The recorded test picture is digitised, and deviations from a stored reference picture are
determined by means of the picture assessment algorithms described below. An alternative
methodology computes the deviation from specific features determined on both the reference
picture and the picture to assess.
5 Methodology for detection of analogue picture degradations
5.1 General
Analogue picture degradations are defined as:
– superimposed patterns, moiré patterns;
– loss of luminance and contrast;
– loss of colour;
– loss of synchronization.
5.2 Algorithm for superimposed patterns, moiré patterns
To assess picture degradation showing as a set of lines, an average signal value is formed for
a defined area in the simplest case. The deviation of the average signal value of this area from
a reference value determined from an undisturbed picture serves as a criterion of whether or
not picture degradation is present.
For picture assessment according to this method, the colour bar pattern according to ITU-R
BT.801-1 used for the test is first converted to a suitable colour space, preferably grey. From
each colour bar, a rectangular section is taken into which a rectangular test window is positioned.
The test window is divided into column segments (see Figure 1).
To reliably detect picture degradation, the test window is rotated by a constant angular
increment of 2° until 180° is attained. For picture degradation in the form of lines, the test
window segments will, in the case of one of the angles measured, be aligned approximately
parallel to the interference lines, thus producing a significant deviation from average signal
values.
As already mentioned, an average value or column sum is determined for each segment, and
the deviation from a reference value is calculated. In addition, a regression line over all column
sums is formed, and the square offset of each column sum from the regression line is calculated.
If the sum of the square offsets of the column sums exceeds the corresponding value
determined for a reference picture, a superimposed pattern or moiré pattern is detected.
The position and size of the test window, or the test window diagonal, should be selected such
that, for any angular position of the test window, all pixels of the test window are located within
the same colour bar of the test pattern, i.e. within the homogeneous area of the colour bar. This
excludes impairment of results by influences from the cross-colour region.
After a 180° rotation, the test window is shifted vertically, i.e. parallel to the borders of the
colour bar section, by a length increment of half the test window height. In its new position, the
test window is again rotated by 180°, and a measurement is performed for each angular
increment. By repeatedly shifting the test window within the colour bar section, virtually the
complete colour bar is covered.

Figure 1 – Colour bar pattern with test elements
for detection of analog picture degradation
The above procedure is repeated for each colour bar of the test pattern.
To cover the complete colour bar pattern, the cross-colour transition regions between the bars
also have to be analysed. For this purpose, the cross-colour transition regions are divided into
vertical segments of seven pixels in height. For each segment, the position of the colour
transition edge and its deviation are determined. The evaluation is performed on the assumption
of a Gaussian distribution of the colour transition edge in each segment over a defined number
of reference pictures, preferably 20 and based on a confidence interval of five times the
standard deviation, σ. Picture degradation is present if the position of colour transition in a
segment is found to be outside the confidence interval of the corresponding reference segment.
5.3 Algorithm for loss of luminance and contrast
As described under 5.2, the colour bar pattern is converted to a suitable colour space, preferably
grey. Any change in luminance or contrast directly affects the grey level, so this type of
degradation can be reliably identified. The algorithm detects grey-level errors if the grey level
deviates by a minimum of ± 5 grey tones from the grey level of the stored reference picture
based on a quantisation of 8 Bit (256 grey tones).
5.4 Algorithm for loss of colour
Colour errors are identified by checking the hue of each colour contained in the colour bar
pattern. To this effect, the test window is additionally converted to another colour space,
preferably HSL. The algorithm detects colour errors if the hue deviates by a minimum of ±10°
from the value of the stored reference picture.
5.5 Algorithm for loss of synchronisation
Synchronisation errors or total sync loss manifest themselves by the loss of one or more colour
components. The algorithm detects loss of colour components if the hue deviates by a minimum
of ±10° from the value of the stored reference picture. Total loss of sync is present if the grey
level drops below 30 in all colour bars (black screen).

– 8 – CISPR TR 29:2020 RLV © IEC:2020
6 Methodology for detection of digital picture degradations
6.1 General
Digital picture degradations are defined as:
– blocking;
– frozen patterns, stop of moving element, blocking in moving element;
– total loss of picture, irrecoverable data stream error.
6.2 Algorithm for blocking
Blocking is characterised by the loss of macroblocks (e.g. 8 pixels × 8 pixels). The edges of
lost macroblocks show as interfering lines that can be detected using the method described
under 5.2. Rotating the test window is not necessary here as the loss of a macroblock always
produces interfering lines at 0° and 90°. The test window size can therefore be expanded to
extend over the full colour bar width so that lost macroblocks will be reliably detected also in
the cross-colour regions.
6.3 Algorithm for frozen patterns, stop of moving element
Frozen patterns are characterised by a stop of the moving element video images that fail to
update over time. To detect this type of picture degradation, a colour bar pattern according to
ITU-R BT.801-1 is used that includes a rectangular moving element (see Figure 2). The
movement of this element is effected by partially shifting the green-to-magenta colour transition
by one pixel per frame, the maximum shift being 40 pixels in either direction. The movement of
the colour transition (edge) is monitored by means of an edge filter. A stop Freezing of the edge
movement is detected if there is no change in the position of the colour transition while the
picture is continuously being captured.

Figure 2 – Colour bar pattern with moving element
for detection of digital picture degradation
6.4 Algorithm for total loss of picture, irrecoverable data stream error
Total loss of picture means a drastic change in the grey-level characteristics. This type of
picture degradation can also be interpreted as extreme blocking and therefore be detected by
means of the algorithm described under 6.2.

7 Alternative methodology for detection of digital picture degradations
7.1 Test pattern
To be able to detect frozen pictures due to digital degradations, CISPR 20 recommends the use
of it is recommended that a colour bar test pattern including a moving element is used. The
following described quality assessment method uses the test pattern represented in Figure 3.

Figure 3 – Alternative colour bar pattern with moving element
for detection of digital picture degradation
The test pattern is composed of the common colour bar in compliance with ITU-R BT.801-1,
with a moving black and white disk. The disk describes a horizontal translation with a period of
30 pictures.
7.2 Analysis
7.2.1 General
The methodology is a reduced reference approach. This is based on computation of specific
features that are sensitive to perceived video impairments. Features are first computed on
pictures from the equipment without stress with no disturbance applied, and then compared
with the same features computed on pictures from the equipment under stress with the
disturbance applied. Comparison of the features provides an indication of perceived video
quality. The video quality is then converted in binary using a computation-based evaluation to
raise alarms.
In the first step, impairment features are extracted. They seek to which represent typical
encoding and transmission error impairments. Usual impairments include blocking effects, false
edges, and empty or misplaced macroblocks, to black or frozen areas. Each of these
impairments affects picture contents in a specific way. Based on this assumption, a set of four
parameters that seek to track these impairments has been defined, with computationally
efficient algorithms. The process analyses all pictures in real time to track all transmission
errors.
These parameters are based on a discrete cosine transform (DCT) blocks-based transform.
Such an analysis in the transformed domain has the advantage of being close to the principle
that MPEG uses for the compression, and to integrate other features like the human eye
sensibility sensitivity to spatial frequencies. Each feature is computed on the DCT luma
component blocks of the picture.

– 10 – CISPR TR 29:2020 RLV © IEC:2020
7.2.2 Automatic spatial synchronisation
A preliminary algorithm of spatial synchronisation is performed, in order to compute features at
the same position for the reference picture and for the current signal. This algorithm is based
on the computation of several horizontal gradients all over the picture and merged in a mean
gradient sequence for each horizontal position studied. The gradients sequence is then
converted into a frequency domain. The frequency sequence is used to match the region of
interest with the picture, excluding 16 pixel borders at the bottom, top, left and right. The same
synchronisation algorithm is processed for both the reference signal and the signal under test,
which guarantee a successful synchronisation between the reference computation and the
computation of the signal under test. This procedure is particularly efficient because it prevents
the user from performing long and difficult alignment of signals.
7.2.3 Spatial activity parameter
The first and the second features represent the spatial activity of the picture. The first one The
spatial activity parameter is dedicated to the global content of the picture, calculated as the
mean of the picture. This allows easy detection of black pictures.
7.2.4 Temporal activity parameter
The third feature next parameter deals with temporal activity. It is based on the integration of
the spatial activity for several pictures. The spatial activity sequence is transformed into
frequency and then summarised into a squared sum to obtain one value for the length of the
sequence. This feature is really especially efficient to detect at detecting frozen pictures, for
example, or jerky motion.
7.2.5 Blocking effect parameter
The last feature parameter is the blocking effect indicator. It is sensitive to DCT patterns and
perceptually annoying block edges. The blocking effect parameter is the ratio of the spatial
activity computed for the picture to the spatial activity computed for the picture shifted in a
translation equivalent to half of the block diagonal. Thus, the ratio of shifted spatial activities is
particularly relevant for enhancing the increase or loss of the block’s edge.
Once these reference parameters are computed for the source test pattern, they are recorded
on the hard disk of the measurement equipment. The reference has to be recorded for twice
the length of the sequence. The dedicated test pattern is 30 pictures long, so the reference-
recording step takes only 60 pictures, i.e. for less than 3 s. Parameters are also computed on
the current video signal, and then compared.
7.3 Comparison
In the second step, the equipment realises the synchronisation of synchronises the incoming
signal (delivered by the receiver under test), with the recorded reference data. This procedure
is automatic, it does not require any other information than the incoming signal and is realised
achieved in a very short time.
Finally, compara
...