Automated optical inspection system for digital TV sets
© Kastelan et al; licensee Springer. 2011
Received: 2 June 2011
Accepted: 23 December 2011
Published: 23 December 2011
This article proposes a real-time test and verification system for full-reference automatic image quality assessment and verification of digital TV sets. Digital camera is used for acquisition of the TV screen content in order to ensure quality assessment of the content as perceived by the user. Test has been executed in three steps: image acquisition by camera, TV screen content extraction and full-reference image quality assessment. The TV screen content is extracted from the captured image in two steps: detection of the TV screen edge and transformation of the TV screen content to dimensions of the reference image. Three image comparison methods are incorporated to perform full-reference image quality assessment. Reference image for quality assessment is obtained either by grabbing the image from TV set or by capturing the TV screen content on the golden sample. Digital camera was later replaced with DSP-based camera for image acquisition and algorithm execution which brought significant performance improvements. The comparison methods were tested under constant and variable illumination conditions. The proposed system is used to automate the verification step on the final production line of digital TV sets. The time required for verification step decreased by a factor of 5 when using the proposed system on the final production line instead of a manual one.
Keywordssub-image extraction image comparison functional failure detection digital TV testing TV screen capturing
In the recent years, it has been shown that manual verification of digital TV systems is not effective for large industries . The overall complexity of the products is increasing exponentially and, on the other hand, the major goal is to keep error rate in the proximity of zero. As a result, some automated systems for digital TV testing have been proposed [2, 3]. The objective of these systems is to optimize the effort of testing and therefore to automate the most parts of the testing process. An automated fault diagnosis becomes an ongoing demand for new technology. The major challenge in designing automated testing systems is achieving acceptable levels of reliability--the system must be able to detect errors without false positives and with a very low rate of false negatives. False positives are faulty TV sets which pass the tests and false negatives are functional TV sets which fail the tests. The system should also bring significant improvements in the speed and cost of testing, in order to be acceptable in television industry.
In order to measure image quality, Sheikh and Bovik  propose an image information fidelity measure that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image. Russo et al.  give a vector approach to image quality assessment. Other approaches for measuring image quality can be found in [6, 7].
This article proposes an approach for an automated verification of digital television sets based on TV screen content acquisition by camera and comparison of the captured content with the content of the reference image. Recent automatic systems for functional verification of digital TV sets use the grabber to capture the content of the TV memory and compare it to the reference content . This approach does not provide verification of the TV screen content seen from user side, only verification of the TV screen content represented in the memory. While grabbing the TV memory content is easier, we propose the usage of camera to acquire the TV screen content in order to ensure quality testing of the content as seen by the user. The camera usage allows detection of problems arising in the circuits between the TV memory and the screen, i.e., when the image on the screen does not correspond to the image in the TV memory and when the TV functional operation fails. The system is based on the algorithm which extracts the content of the TV screen from the captured image and compares it with the reference images [9, 10]. The system is used as part of the Black Box Testing (BBT) system [1, 8].
The algorithm for TV screen extraction and comparison is based on the following image processing problems: line detection, rectangle detection, image transformation, and image comparison.
Line detection is the subject of many related studies. Lagunovsky and Ablameyko  propose the line and rectangle detection by clustering and grouping of linear primitives. They extract line primitives from image edges by linear primitives grouping and line merging. Marot and Bourennane  propose a formalism to transpose an image processing problem to an array processing problem. They performed straight-line characterization using the subspace-based line detection (SLIDE). Both of these methods are computationally intensive and, due to simplifications imposed by the nature of our system, they are unnecessarily complex. One popular method for line detection is the usage of Hough transform. Duan et al.  propose an improved Hough transform, which is the combination of the modified Hough transform and the Windowed random Hough transform. They modify the Hough transform by using the mapping and sliding window neighborhood technique. Another approach using the Hough transform is given by Aggarwal and Karl  which uses the inverse of Radon operator, since the Hough transform is the special case of Radon transform. Hough transform also provides unnecessary computational complexity and even though it gives reliable rectangle detection, it does not pose a suitable method for our system due to the curvature of TV edges and other non-uniformities in the system. Therefore we design our own method for line detection which is computationally simpler, but more reliable under the conditions imposed by our system. Other interesting approaches to line detection are given in [15, 16].
Hough transform is also widely used as a tool in rectangle detection. Jung and Schramm  present an approach to rectangle detection based on windowed Hough transform. In order to detect rectangles, they search through Hough domain for four peaks which satisfy certain geometric conditions, such that they represent two perpendicular pairs of parallel intersecting lines. Other approaches to rectangle detection are presented in [18–20].
Image transformation and scaling are techniques widely used in digital television industry. Leelarasmee  gives the architecture for a TV sign image expander with closed caption encoder. It allows nine image scaling factors ranging from 1 × 1 to 2 × 2. Hutchison et al.  present application of multimedia display processor which provides a cost effective and flexible platform for many video processing algorithms, including image scaling. In order to overcome the problems such as blurring and jagging around the edges, Liang et al.  propose a coordinate rotation and kernel stretch strategy combined with the bilinear or bicubic algorithm. Transformation of image captured by camera is one way of document digitization. Stamatopoulos et al.  present a goal-oriented rectification methodology to compensate for undesirable document image distortions. Their approach relies upon a coarse-to-fine strategy. Very Large Scale Integration (VLSI) implementation of image scaling algorithm is presented by Chen et al. . Other types of image transformations can be found in [26–28].
Sun and Hoogs  present a solution for image comparison which uses compound disjoint information. They analyzed their results in the problems of image alignment, matching, and video tracking. Osadchy et al.  study the surface-dependent representations for image comparison which is insensitive to illumination changes. They offer a combined approach of Whitening and gradient-direction-based methods. Matungka et al.  present an approach to image comparison which uses adaptive polar transform, which they derived from log-polar transform. The adaptive polar transform effectively samples the image in Cartesian coordinates. They perform acceleration using the Gabor feature extraction. Other approaches to image comparison are presented in [32–34]. All of these methods bring enough reliability, but they are computationally complex. Considering that our system is not pixel-sensitive, i.e., we do not need to detect faults in individual pixels, but instead functional failures which are always presented as a wrong screen content which differs from the reference image in a whole region, we propose regional-based image comparison methods which are computationally simpler but reliable-enough for our system application.
This article presents and analyzes three methods for image comparison with the goal of finding the optimal method for the system. The first two are standard image comparison methods: least-absolute-error method (LAE) and normalized cross-correlation method (NCC). The third method is the block-based modification of the normalized cross correlation, which introduces the golden sample and makes comparison scores relative to the score of the golden sample. It was designed to be more sensitive to small differences between the images, compared to the first two methods. The comparison score is used in making decision if the image on the TV screen is correct and if the TV set is functioning correctly.
The system was designed in three versions: first, the regular camera was used to capture the image and personal computer (PC) was used to run the extraction and comparison algorithm. Next, the regular camera was replaced with the DSP-based camera in order to increase the speed of image capturing. Finally, algorithm was implemented on the camera DSP, removing PC from the system, which brought significant performance improvements in algorithm execution with the goal of achieving the real-time execution.
The proposed system is used to automate the verification step on the final production line of digital TV sets. To the best of our knowledge, the verification step on the final production is mostly performed manually, by a human observing the TV screen. The TVs which are being tested are coming on a production line and passing through several test stations. Each station tests a particular part of the TV system, e.g., component mount control, High-Definition Multimedia Interface (HDMI) or SCART. Each station has a person working on it. The worker's job is to select desired test sequences and detect faults on the TV screen by directly observing the TV screen and reporting if that particular TV passes or fails the tests. Since the current method of verification is manual, many subjective errors are possible. Also, the speed of a manual verification system is slow. The worker needs to perform manual and visual check of the TV screen as well as to connect the TV set to a particular signal generator. The proposed system aims to eliminate the need for many human workers at the verification step on the final production line, aiming to automate the verification process. The time required for verification step decreased by a factor of 5 when using the proposed system on the final production line instead of a manual one .
The rest of the article is organized as follows: first, the system overview is presented. The detailed explanation of the central part of the system, the TV screen extraction and comparison algorithm, follows. Three methods for image comparison: LAE, NCC, and block-based normalized cross-correlation (NCC-BB) are explained and compared. Next, DSP-based implementation of the proposed system is presented. Finally, experimental results are presented with some concluding remarks.
2 System overview
The captured and the reference images are used as inputs to the detection and comparison algorithm, presented in the following sections. The main challenge in algorithm design was to make a robust method of detecting the borders of TV screen and transform the TV screen content from the captured image to the dimensions of the reference image. The two images need to have the same dimensions for comparison. Transformation is the crucial part before the comparison can be performed because the TV screen content does not appear as a rectangle in the captured image. Instead, it appears as a slightly curved quadrilateral due to the curvature of the camera lenses and relative orientation of the camera and the TV screen plane. The transformation problem is addressed and transformation equations are derived in algorithm section. The output of the algorithm is the similarity measure of the two contents. That output is used in making the decision about the matching of the two contents, as discussed later.
The black chamber is used as an integral part of the system, to control illumination conditions. The TV is brought inside the chamber, the camera inside the chamber captures the state on the TV and the TV leaves the chamber on the opposite side. After automating the verification, its speed would significantly increase. The proposed automated verification approach reduces the amount of manual work on the verification step in TV industry. The manual work is required only for connecting and disconnecting the TV to signal generators. The subjective errors are eliminated and the reliability of tests increases. The benefits of the proposed system in industry application and the proposed testing methodology implemented by the system are analyzed in detail in . While the reference  focuses on industry application, compares manual and automatic verification and presents testing methodology on the final production line, this article presents in more detail the verification and quality metrics of video, as well as the DSP implementation of the proposed system with the goal of achieving real-time execution.
3 Algorithm for TV screen content extraction and comparison
3.1 TV screen content detection and transformation
The first step in the algorithm is the reduction of noise by the Gaussian method . In image A, the noise is reduced using the convolution defined by the Gaussian method of noise reduction.
The second step in the algorithm is the general edge detection using the Scharr operator . This operator is said to have improvements over the widely used Sobel operator. After calculating the intensity and angle of the edges, threshold is applied on both values. Only edges with enough-high intensity and those with the angle in the neighborhood of the values 0 and (approx. horizontal and vertical) are kept for the future steps.
The third step in the algorithm is the detection of long horizontal and vertical lines. Due to the non-ideal positioning of the camera and the curvature resulting from the camera lenses, the lines are not horizontal or vertical, but a bit curved. For that reason, the lines are detected inside a buffer, which allows curvatures to be detected. The buffer represents the neighborhood of the points on the line. Using the buffer allows for small curvatures and discontinuities to be neglected, which result from non-ideal camera lenses and edge-detection threshold.
The reference images are in a predefined resolution, 1920 × 1080 in HDTV standard. In order to compare the extracted TV screen content with the reference images, it is required to transform it to the dimensions of the reference image. The complications arise not only in the fact that the vertices of the TV screen edge form a quadrilateral which does not have to be a rectangle or not even a rhomboid, but also in the fact that the sides of that quadrilateral are curved, due to the curvature of the camera lenses.
The transformed image does not have to be perfectly interpolated for comparison, because the comparison will be regional-based and not pixel-based. This constraint allows the simplifications in the transformation mathematics, which will be explained in this section.
In order to better understand the transformation performed in the proposed algorithm, this article proposes the method for transforming the image from the rectangle dimensions 1920 × 1080 to the TV-screen-edge-bordered area on the captured image. The algorithm performs the reverse of the proposed operation, because the comparison is made on 1920 × 1080 pictures, but the former direction of transformation is easier to understand.
As mentioned, TV screen edge does not represent any regular geometric shape. When its vertices are connected with straight lines, they form a general quadrilateral. The first step is transforming the rectangle into a quadrilateral formed by connecting the vertices of the detected TV screen edge.
We can assume that points A and A' are in the origins of the respective coordinate systems. One of the sides of the quadrilateral A'B'C'D' can be fixed without loss of generality. We will fix the side A'D' to be vertical.
After transforming the rectangle into a quadrilateral, it is required to fit it in the TV screen edges which are curved. The fitting is performed first in one dimension and then in the other line by line. Each line is extended to fit the new edges.
The proposed algorithm performs the transformation in the opposite direction than the one described before, transforming the content of the TV screen to the rectangle 1920 × 1080. It is done by simply reversing the process explained there, first by contracting each line in both dimensions from curved edges to edges of a quadrilateral, and then using inverse Equations (1)-(7) to transform the quadrilateral to the rectangle.
3.2 Image comparison methods
The comparison of the test image with the reference image is performed in the dimensions of the reference image, which in HDTV standard is 1920 × 1080. In this section three techniques for comparison are presented, one based on LAE and two based on NCC.
3.3 LAE method
The first method used for comparison is the regional-based LAE method. The image is divided into regions which are considered atomic. Each region in the test image is compared with the respective region from the reference image.
3.4 NCC method
This method is based on the cross-correlation as a measurement of the similarity of the two images. The normalization is performed to reduce dependence on illumination.
For discrete signals, integrals change to sums. Each region is processed independently and the similarities for each region are accumulated. The similarity measures are calculated for each color component and accumulated.
3.5 NCC-BB method
The problem with applying the NCC method is impossibility to define the absolute threshold in the similarity score. It is a problem because the score on an image got by NCC method is dependent on the image content. In order to overcome this problem, an improvement to the NCC comparison method is presented here. It computes the relative similarity score, instead of the absolute one computed by the NCC method. The proposed method is the NCC-BB which performs NCC comparison in blocks of an image, not the whole image. "Blocks" in the name of the method are not the same as "regions" mentioned in the previous two methods. Regions are parts of the image considered atomic, i.e., they are assigned one (R,G,B) value. Blocks are larger parts of the image for which NCC score is calculated and they consist of previously mentioned regions. In the NCC method, the whole image is one block. In the NCC-BB method, image is divided into several blocks whose NCC scores are independently calculated.
First, the correct image captured by camera is fed to the algorithm for each test case; the NCC-BB algorithm computes NCC similarity scores for each block in the image and stores them for future reference. This "learning" step does not reduce the level of automation of the system because it needs to be performed only once for each test pattern, e.g., during system installation. A correct TV set is chosen to represent the golden sample in order to capture the image on its screen by camera. After these initial tests are run and the system installation is complete, all other TV sets are tested relative to the results of the golden sample.
Using NCC comparison on smaller blocks allows for smaller differences in the image to be reflected with the larger difference in similarity score. The use of a golden sample makes similarity score relative, instead of absolute. These improvements allow the Definition of the absolute threshold in the pass/fail decision part of the algorithm, a value not easily definable in the original NCC comparison method.
Blocks are distanced a constant number of pixels from each other, which is unrelated to the size of the block, i.e., it may be equal, smaller or even larger than the block size, although the last one is not practical because it skips parts of the image. The block is moved along the X coordinate first and when the right end of the image is reached, block is moved along the Y coordinate and set to the left end. Iteration ends when the block reaches the bottom-right corner of the image. The size of the block for full High Definition image (1920 × 1080) was chosen to be 512 × 512 with the sliding step 80%. The sliding step is the distance between blocks relative to the region size.
4 Implementation on dedicated DSP platform
The proposed verification system was designed in three ways: (1) image capturing with regular digital camera and algorithm execution on the PC, (2) image capturing with DSP-based Texas Instruments (TI) IPNC DM368 camera and algorithm execution on PC, and (3) image capturing and algorithm execution on DSP-based TI IPNC DM368 camera.
In the first implementation, digital camera was used to capture the image and send the image to PC where algorithm execution is performed. The communication between the camera and the PC is done through the universal serial bus (USB) interface. This implementation was the first solution. It is used as a reference implementation and is expected to have the slowest time of execution.
5 Experimental results
This section summarizes the main experimental results in (1) comparison methods (LAE, NCC, NCC-BB), (2) algorithm implementation on PC and dedicated DSP platform. Verification times on the final production line in industry and improvement of the proposed verification approach relative to the manual verification approach are discussed in detail in . The speed of verification step is increased by a factor of 5 when the proposed automatic approach on the final production line is used instead of a manual one.
5.1 Results of comparison methods
The experiments of comparison methods were performed in order to verify the success of each method and to choose which method is better for detecting the content on the TV screen. The methods were first tested with the test set featuring some common TV patterns and menus. The methods were then tested with images in normal environment, captured by the camera in the constant illumination conditions. The final set of tests was performed under different illumination conditions which were not constant throughout the TV screen.
Results of the three comparison methods in pattern tests
Results of pattern tests
Different pattern 1
Different pattern 2
Different pattern 3
Results of the three comparison methods in menu tests
Results of menu tests
Correct menu and selection
Same menu, different selection 1
Same menu, different selection 2
Results of the three comparison methods in image tests under constant illumination
Results of image tests - constant illumination
Different image 1
Different image 2
Different image 3
Results of the three comparison methods in image tests under variable illumination
Results of image tests--variable illumination
Similar image 1
Similar image 2
Table 1 shows the results of pattern tests between the three methods presented in this article. It can be seen that all three methods correctly detected the reference image whose content is present on the TV screen. It should be noted that LAE method measured the dissimilarity of the two images, while NCC and NCC-BB methods measure the similarity of the two images. Hence, the correct image has the lowest score under LAE, highest score under NCC and the score closest to 0 under NCC-BB method, because the NCC-BB score is relative to the golden sample which has the score 0.
The next set of tests was performed with images under constant illumination conditions. These conditions mean that the test image does not necessarily have the same brightness as the reference image, but the brightness of the test image is constant throughout the image. Due to constant illumination, normalization is expected to eliminate the difference in brightness and allow a content-only comparison. Table 3 shows that these conditions are manageable in all three methods of comparison and that correct reference image was detected.
Even though extreme conditions which are avoidable in test environments showed vulnerability of the NCC-BB method, the real advantage of NCC-BB method is in that it gives the relative score which makes the definition of absolute pass/fail threshold much easier. In the other two methods the score largely depends on the image itself and defining the absolute threshold for all images is difficult, if not impossible. Therefore, NCC-BB method was chosen to be most suitable for industry application of this verification system.
5.2 Results of DSP implementation
* GV-698--verifies RF input interface,
* CVBS1--verifies video interface on TV input EXT1,
* CVBS2--verifies video interface on TV input SideAv,
* HDMI1--verifies video interface on High Definition Multimedia Interface input 1,
* HDMI2--verifies video interface on HDMI input 2,
* YPbPr--verifies video interface on YPbPr input,
* VGA--verifies video interface on Vector Graphic Array input,
* CVBS3--verifies video interface on TV input EXT2,
* SVIDEO--verifies video interface on S-input,
* USB--verifies Universal Serial Bus interface.
Execution times of the three versions of the system in 10 example tests
Execution times on example tests (in seconds)
Regular camera + PC
DSP camera + PC
DSP camera optimized
Execution times of algorithm steps in the three versions of the system for the HDMI2 test example
Execution times on HDMI2 test example (in seconds)
Regular camera + PC
DSP camera + PC
DSP camera optimized
TV screen extraction
The proposed algorithm for TV screen content detection and recognition was successful in recognizing the TV screen content under different illumination conditions. The NCC method for image comparison was robust-enough to recognize the content even under variable illumination conditions with strong brightness in the part of the image. LAE and NCC-BB methods were vulnerable for detecting the small differences under variable illumination, but they were successful under less strict conditions. NCC-BB is the best for industry application because of its relative score and the fact that variable illumination can be avoided in controlled test environments. Due to the high controllability of the environment in the test systems, all three methods may be used as part of the algorithm. Since the comparison part is not the bottleneck of the algorithm, all three methods may be used together in order to make the results more reliable.
The proposed verification method significantly increased the speed of verification on the final production line, by a factor of 5 . Proposed implementation on dedicated DSP platform further increased the speed of execution.
The future study will consist of improving the steps of the algorithm to achieve better robustness. One idea is to dynamically change thresholds during TV screen edge detection, to allow adaptation in changing environments. Additional work will be done to improve robustness on the relative orientation of the camera and the TV screen plane. Other methods for comparison may be developed with better robustness on different lighting conditions.
This study was partially supported by the Ministry of Education and Science of the Republic of Serbia, under the project No. 44009, 2011.
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