TWI226189B - Method for automatically detecting region of interest in the image - Google Patents

Method for automatically detecting region of interest in the image Download PDF

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Publication number
TWI226189B
TWI226189B TW92107091A TW92107091A TWI226189B TW I226189 B TWI226189 B TW I226189B TW 92107091 A TW92107091 A TW 92107091A TW 92107091 A TW92107091 A TW 92107091A TW I226189 B TWI226189 B TW I226189B
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Taiwan
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image
interest
area
small
roi
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TW92107091A
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TW200420115A (en
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Tz-Chiang Chen
Jr-Chang Chen
Hung-Shin Wu
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Nat Univ Chung Cheng
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Abstract

The present invention relates to a method for automatically detecting the region of interest (ROI) in the image. In the method for automatically detecting ROI in the image, by conferring the coefficient after conversion of the image in the compression process, the incorporation of the variations of position and frequency of the coefficient block, and the compression bit rate requirement, detection is automatically conducted to generate the ROI of an image-related content variation. The bit flow of the related application is encoded for the different compressing process selections of the specified region based on each compressed standard so as to raise the vision quality of the ROI image after the decompression. In addition, the coefficient data after analysis and conversion is proposed, and is sorted into a small unit block so as to locate the center point of the interested small unit block assembly and use image processing skill to generate a continuous and closed mask for the ROI. The present invention is capable of having more adaptable characteristic for the requirement of the image content and the compressed bit rate. In addition, because the generation mechanism of the ROI mask is embedded in the compression procedure, the present invention also has the characteristic of low calculation cost.

Description

1226189 发明, invention is ugly '(Invention Day I should state: the technical field, prior art, content, implementation, and drawings of the invention briefly explain) [Technical field to which the invention belongs] The present invention relates to an automatic detection The method of measuring the region of interest (Region 〇 Interest;-ROI), especially a method of automatically generating the mask of the region of interest, can be more adaptable to the image content and compression bit rate requirements. Users can obtain better visual effects and image quality in the ROI region after decompression. [Previous technology] _ With the rapid progress and development of information technology, we have entered a variety of information ages, and a large number of images, text, and audio / video data are transmitted through wired or wireless channels, including through individuals Mobile communication systems, the Internet and other media, to achieve real-time transmission and reception of these multimedia information; on the other hand, storing such a large amount of multimedia data is also a difficult problem. In terms of video or audio / video data transmission or storage, uncompressed data will not be able to achieve the purpose of real-time transmission and effective storage due to the large data capacity, so compression of multimedia data becomes a necessary step. The Joint Photographic Experts Group (JPEG) Order, established under the ISO and ITU-T organizations, was established in 1986. It is mainly dedicated to the development of compression standards for still images. The current universal JPEG image compression standard was first developed for this organization, followed by JPEG. 2000 is the recently developed Image® compression standard. In the current image and video compression standards, Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) are used to reduce spatial redundancy of images or video data, including images. Compression standards, quasi-JPEG and JPEG 2000, and video compression standards MPEG-1, MPEG-2, MPEG-4,

H.261, H.263 and H.263 +, etc. For the image compression standards JPEG and JPEG 2000, the required conversions are discrete cosine transform and discrete wavelet transform. The former uses 8x8 pixel blocks as discrete Cosine transformation processing unit, and encode it into a bit stream after conversion; the latter uses the entire image as input for discrete wavelet transformation, and the converted coefficients are divided into N 11 1226189 content for EBCOT (Embedded Block Coding with Optimized Truncation) ) Encoding, including steps such as bit plane and geometric coding, and encoding into a more efficient embedded bit-stream. The entire JPEG 2000 compression process is shown in the second figure, and its processing flow can be roughly divided into the following three steps: 1. Pre-processing of the input image, this step includes image dividing (tile dividing) and color transform (color transform). Cut the input image size according to different needs, and perform color conversion on the cut image as a unit. 2. The image data after color conversion is then subjected to discrete wavelet conversion (Discrete

Wavelet Transform (DWT) to remove redundant signals in the frequency domain and perform quantization ° · 3. Finally, the quantized discrete wavelet parameters are processed in bit plane units to remove the redundant signals in the bit domain (bit redundancy), and the data stream is output in packets, which is called EBCOT coding. In JPEG 2000 part 1, it also provides ROI coding options to enhance the coding performance for specific areas in the image. The principle is to sacrifice the quality of the non-interest area and improve the image quality in the area of interest. During the transmission, the image content in the area of interest is displayed first. Since the image is encoded by the ROI option in JPEG 2000, the ROI can be preferentially encoded into a data stream, and the image data stream in the ROI can still have a certain quality through channels with insufficient bandwidth. Therefore, this technology is applied in the Internet Multimedia interactive displays on the Internet or wireless communications are extremely important. In general ROI applications, when an image is divided into a region of interest (ROI) and a region of non-interest (background), the ROI position information must be added during encoding to facilitate the decoder to output the correct ROI position. However, In JPEG 2000 part 1, one is called _

With MaxShift's ROI coding technique, it is not necessary to transmit the relevant ROI position information between the bit streams, and the decoder can still determine the ROI position. When applying the compressed image data of ROI function, it is hoped that different image processing or compression techniques can be applied to specific objects or areas in the image in order to improve the quality of this specific object or area. 13 1226189 Quality, specific objects or areas in this image It is called Region of Interest (ROI). For example, more pixels are used to represent pixels in the area of interest. 高 High-resolution is used to enhance the sharpness of the image in the area of interest. Small quantization 値 to quantize coefficients and so on. The above methods can enhance the visual effect of the image after decompression. However, for the selection of areas of interest in the image, in general, the classification and identification of specific objects or areas in the image are performed using image processing, and the manual selection is used. There is a fixed area of interest. Therefore, there are two problems to be solved depending on the way in which these two methods are generated according to the area of interest. (1) · Manually select the fixed area of interest in the image. The method 圏 selects the areas of interest that have the same shape and size. Therefore, for image compression applications, the size and bit rate requirements of the areas of interest will not have the adjustment function. (2). The area of interest is obtained by separating and identifying the image in advance. Before the image is processed, an area of interest is obtained through the process of image separation or identification. Usually, the image recognition must take a considerable amount of calculation. In addition, this method is also suitable for The compression bit rate requirement is not adaptable to the size of the area of interest. Therefore, based on solving the above problems, the inventor of the present case proposes a method and device for analyzing the content of the coefficients of the image data after conversion, and having a method and device for automatically detecting the region of interest in the image. The content of the converted coefficients and the compression bit rate requirements determine the size and position of the ROI region of the relevant image content. Users can obtain better visual effects and image quality in the ROI region after decompression. [Content] The main purpose of the present invention is to provide a method for automatically detecting an area of interest in an image. The present invention is based on the content of the converted coefficients of the relevant frequency changes obtained when the image data is compressed, and it meets the compression bit rate requirements. , Determine the size and position of the ROI region of the relevant image content, and users can get better visual effects and image quality in the ROI region after decompression. A secondary object of the present invention is to provide a method for automatically detecting an area of interest in an image. The method 14 1226189 can automatically adapt the image content and compression bit rate requirements. The entire ROI mask generation mechanism is embedded in the compression process, and the invention also has the characteristics of low calculation cost. Yet another object of the present invention is to provide a method for automatically detecting an area of interest in an image. The ROI generation mechanism is embedded in the image compression process without the need for additional manual or image recognition techniques. And it can be used in various image input or compression processes, such as medical images, infrared images, and so on. The present invention proposes a method for analyzing coefficients after image conversion. It explores the position and frequency changes of the coefficient encoding blocks. In accordance with the compression bit rate requirements, a Roi mask is generated during the compression process. This mask covers the image. The area of interest can be compressed and encoded, depending on the application area, the relevant coefficients are processed for the converted coefficients in the ROI mask, and finally encoded into the bit data of the embedded ROI information. [Implementation] In order to make your reviewing members have a better understanding and understanding of the structural features and achieved effects of the present invention, I would like to refer to the preferred embodiments and detailed descriptions as follows: In order to avoid the use of images The over-design of the ROI area caused by identification, the labor cost of manually generating the ROI area, and the lack of adaptability to the image content. In the present invention, the frequency of the image conversion coefficients is analyzed during the image compression process. Characteristics, automatically obtain the ROI region when image encoding and compression, please refer to the third figure, which is a flowchart of a preferred embodiment of the present invention; the proposed processing steps are as follows: Step 10, using the coefficients after conversion Analysis of frequency and location, classify small unit blocks. According to the frequency and position of the transformed coefficients of the coded block, the image is determined by using the ρχρ pixel small unit block as the basic unit for analysis, to determine whether this small unit belongs to the R01 region, where p is the time when the image judges the block of interest. The basic unit depends on the significance of the coefficients after the conversion or the compression process. Step 20: Find the center point of the set of small units of interest that has been determined to be of interest in the image. In order to avoid that the small unit blocks that have been determined as areas of interest are scattered in the image, it is necessary to consider the distribution position of the small unit 15 1226189 block in the image, find the center point of this set distribution, and try to make the small block set appear Grouping status, so that the effect of Roi region coding can be revealed; and step 30, a ROI mask is generated. After getting the center point of the small unit block of interest, it is processed with the morphology in image processing, the expansion operation and the erosion operation to obtain multiple presentation clusters but The set of discontinuous small blocks, in order to enable the ROI mask to focus on the small block distribution center points obtained in the above steps, and present a closed and continuous situation. Next, in accordance with the compression bit rate requirements, search from the center point outward and Combine the small block sets after type processing, and decide which block sets become Roi masks related to the bit rate requirements. * The above three method steps are designed to consider the shortcomings of the ROI area obtained by manual or image recognition mentioned above. In step 10, in order to analyze and identify small unit blocks belonging to the characteristics of interest in real time based on the content of the image, in steps 20 and 30, in accordance with the requirements of transmission or compression conditions, the results of step 10 are automatically integrated to obtain A ROI mask, so it has the function of adjusting the bit rate conditions, and try to make the ROI mask appear closed and clustered as much as possible, which improves the visual quality. However, the proposed invention does not emphasize the method or device applied to any particular discretely transformed coefficients or any particular image compression standard. It has a similar analysis of the frequency changes of the coefficients and the position information of the transformed images to generate regions of interest. The analysis methods proposed by the present invention can be used in future image compression standards or discrete conversion coefficients. For the automatic detection of ROI masking, you must first make statistics on the conversion method used, according to the high and low frequency changes of different conversion coefficients, and determine whether it is a suitable area of interest in order to: obtain the distribution of image content; In order to consider the clustering of small unit blocks based on the distribution of small unit blocks, the combination of small unit blocks becomes a closed and as continuous ROI mask as possible under the constraints of bit rate requirements. However, the ROI mask generation method proposed by the present invention is not limited to any kind of compression program. During the compression process, the content of the converted coefficients is explored, and the analysis and classification of small unit blocks are considered in conjunction with 16 1226189. The image has been determined to be in the set of small units of interest. The center point and a ROI mask are generated. In step 10, since the encoding of EBCOT is based on a 4-pixel "stripe" as the coding unit, for the sake of speed and compatibility, we use 4x4 small blocks as the unit of measure in the first step. Let σ〆 /, force represent the bit importance of the coordinate in the kth bit plane (/, force position, and bk (i, j) to determine whether the coordinate position (i, j) belongs to the most important code scan The state of the scanning process indicates that when bk (i, j) is "1", the points belonging to the most important code scanning process are recorded, otherwise bk (i, j) will be recorded as "0". In order to explore each bit Whether the state indicated by the most important encoding scanning process of the meta plane can indicate the content of image changes. The state of the most important encoding scanning process will be calculated from the most important bit plane (MSB) to the least according to the importance of the bit plane. The significant bit plane (LSB) is called ® SumN, which is the sum of the number of significant bits accumulated to the Nth plane. From this result, it can be decided that for any input image, the number of planes should be taken as the data to explore Basis. N- \ nl

SumN = ΣΣfrom, y ') ⑴ / = 0 y = 0 The N in the above formula represents the bit plane of the cumulative calculation of scanning, η is the unit block size when bit plane encoding, usually 64x64 pixel blocks are Master (ie η = 64); It is known from experimental experience that when the accumulated block counts are more than 1/8 of the unit block area of the important encoding process, then this bit plane can be used as a basis for correctly discussing the content of the DWT coefficients. That is, the calculation of SumN is stopped. At this time, N is not set to k. From now on, for the investigation of the bit plane, the distribution of the important encoding process of the 0th bit plane will be calculated and classified. This system indirectly uses the information of EBCOT as input instead of directly taking the discrete wavelet transform coefficients as the classification basis. However, EBCOT is not actually scanning the entire block at a time-but using 4 pixels as a unit, one at a time The sequence of the strip completes the scanning of each 64x64 pixel coded block, and the fifth figure shows the scanning of the strip in the block. Therefore, with such an encoding process, the mechanism of the present invention does not simply consider the bit plane data of the entire block, but classifies the scanning information generated by EBCOT with 4x4 pixel small unit blocks. 18 1226189 The relative classification method will cause the ROI mask to appear in small blocks (rather than any geometric shape) when the rOI mask is generated. However, considering this method, the EBCOT Bit plane importance information, and the ROI mask generated is sufficient to cover the nearby areas with high frequency changes, so that the high frequency changes of the image are displayed more clearly near. The 4x4 pixel small unit block is determined by the number of significant bits contained in the block, that is, the small unit block contains the number of important encoding processes. The calculation formula is as follows: touch " 411 // 4 '' ) = (2) / = 0 _ / = 0 The maximum Bk here is "16" and the minimum Bk is "0", which means that when a coding block is divided into 4 x 4 pixel small blocks, the small blocks are important. The number of encoding processes, and n 値 in the formula is 64, which represents the encoding block (64x64 pixels) when EBCOT encoding, and μ ″ means taking the integer part of the symbol △. After the image is converted by DWT, it represents different meanings in different frequency bands. Therefore, the bit planes belonging to the low frequency and high frequency in the DWT coefficients are respectively counted. The results of the statistics on the low frequency signal parts are Bk, ix, and LLJBAND The statistical results for the high-frequency signal part are Bk, HL, Bk, LH, and Bk, HH, which represent the statistical results of HL JBAND, LH_BAND, and HH_BAND, respectively. The following describes the method of judging these two different frequency bands: ⑻Statistics of low-frequency blocks When exploring the statistical characteristics of low-frequency fast signals, first define a period, which is an important code in the LL_BAND small block. The result of multiplying by ^ weighting ti (weighting), the calculation formula is as follows: = t ^ BkjLL (3) At this time, to determine whether 代表 represents the classification of interest, you need a critical 値 judgment, this critical 値The calculation is to consider the maximum Bk, LL of the entire LL-BAND, and set this critical low-frequency signal to Tf, then Tf can be obtained by the following formula:

Tf = max) / 2 ⑷ Therefore, at that time, we classified this small block as a 19 1226189 region with smooth changes and high brightness 値.统计 Statistics of high-frequency blocks The areas with high-frequency changes are usually the edges of objects in the image, areas with large or sharp changes in texture, and from the perspective of sensory visual effects, usually the area of interest of an image is that the object itself contains edges Partly, therefore, after obtaining a smooth and bright area with low-frequency signal changes, an edge position message surrounding this area is needed, and such information usually appears in areas where the DWT coefficients of the high-frequency bands change widely. So let ’s explore the statistical results of the HLjAND, LH_BAND, and HH_BAND small blocks that are important coding processes. Because these three bands each represent texture changes in different directions, so multiply the number of the three bands that are important coding processes by After weighting 値 t2, t3, and t4, the sum becomes a high frequency change representing this small block:

Bjc = h year Bk HL + 1: / Bk, LH + t4 * Bk, HH (5) And here also a critical threshold is needed to determine whether the high-frequency change of the small block added belongs to the edge of the area of interest, or the image In the sharp region of China, the calculation of this critical 値 Te is also obtained from the statistical results of three high-frequency signals. The calculation formula is as follows:

Te = max ^^ + BkLH + BkHH) / 2 ⑹ When 5f > Te is judged to be a small block of interest with high frequency changes at this time, it will represent the texture or brightness change in all directions in the image, and when these blocks appear more closed When the areas are arranged, the position of the Roi mask can be effectively defined. In addition, in order to consider the adaptability of the image content and the compression bit rate requirements, the acquisition of the weighted threshold and critical threshold in (3) to (2) above can be determined according to the bit rate requirements. In step 20, if the image or image contains an object or a specific high-frequency and low-frequency change phenomenon, the high-frequency surrounding area presented in the image is often easier to be an area of interest when viewing the image, and the viewer is also interested in The image presentation quality here has higher requirements. Therefore, after calculating the distribution of the high-frequency area blocks, we can find the geometric center of the high-frequency block distribution based on the tile distribution. Assuming that the vertical coordinates of the top and bottom positions of the distribution are T_Bk 1226189 and B_Bk, and the left and right horizontal positions of the distribution are L_Bk and R_Bk, respectively, the center point position (m, η) can be determined by these four points. Calculated: (m, n) = (T_Bk ^ B_Bk L_Bk + R_Bk, ⑺ The above formula (m, n) is the center point of the ROI mask obtained based on the high-frequency changes of the image, where h and v are vertical respectively The average coefficient of level and level can determine its weight according to the high-frequency changing block area of the image content, trying to quickly find the position of the center point similar to the center of gravity of the distribution.

In step 30, after the center point of the ROI mask is obtained through the range of the high-frequency block distribution, the high-frequency and low-frequency blocks must be integrated to include the areas belonging to important bits, so that the generated ROI mask has a closed nature. And continuity. Here, a morphology commonly used in image processing is used to obtain a small block set of related regions of interest according to the operation of the formula. ROI_mask = ({B [, B [} 〇C, nC (8)

The above formula ROI_mask is the ROI mask obtained, C is the masking matrix used for erosion and expansion, and "〇" and "Π" represent expansion and erosion operations, where expansion The operation is the result of the combined set of the small block set and the operation mask matrix, and the erosion operation is the result of the intersection of the result of the expansion operation and the operation mask matrix. In addition, in order to obtain the ROI mask related to the compression bit rate requirement, at the end of this step, consider the position of the center point of the small block obtained in the previous step, and use the center point as the starting point to combine the type and process the result. The small block set meets the bit rate requirements to limit the area of the small block set, and forms a closed and continuous ROI mask with the relevant bit rate requirements. The relationship between the ROI mask generation mechanism proposed in the present invention and the entire JPEG 2000 processing block diagram is shown in Figure 6. Compared with the original JPEG 2000 compression process, the proposed mechanism is a ROI mask generator embedded in the image During the compression process, the input image is transformed and quantized by the discrete wavelet to be processed, and the important encoding process position presented by the specific bit plane in the EBCOT encoding process is analyzed. In accordance with the compression conditions (bit rate requirements), a relative The ROI of the image content is masked, so the EBCOT code is re-entered, and the compressed bit-stream is re-arranged. 21 1226189 to form a compressed data stream with RIO region coding, so that users can use limited bandwidth conditions. When the image is decompressed, the area of interest of the image has better image quality. (4) Description of the embodiment As shown in the seventh figure, under the condition that the bit rate requirements of two different images for the two images are 0.4 bpp (bits per pixel), a fixed rectangular Roi mask is used to cover the invention. Automatic detection of the decompressed image presented by the ROI mask. The seventh image A is the original image, the seventh image B is the two images compressed by the original JPEG 2000, and the seventh image C is a fixed rectangular ROI mask. The decompression result when the mask area is 1/3 of the image size. The area surrounded by the white line in the seventh D image is the automatic detection ROI mask proposed by the present invention. The seventh E image is shown in the seventh d image. The image obtained by encoding and decoding the r0i mask in the image; from the results presented by the different ROI mask settings in the image in Figure 7, it can be seen that the detected ROI mask covers the higher part of the pixels in the image This is because it is based on important coding process information in bit-plane coding. When the referenced bit-plane is a low-frequency signal, this method can easily classify blocks with high pixels in the image as regions of interest, and when When the referenced bit plane is a high-frequency signal, the edge portion of the object It can also be easily classified as a region of interest, and under the low bit rate requirement of 0.4 bpp, the compressed image is detected automatically with a roi mask than a fixed roi mask, and the visual quality is better after decompression. The eighth image shows the decompression results of the same image compressed with a fixed rectangular ROI mask and the automatic detection ROI mask method proposed by the present invention under different compression bit rate requirements. The eighth image shows a fixed rectangle The compressed image of the ROI mask, the eighth figure B is the image compressed by the automatic detection method of the ROI mask proposed by the present invention, and the compression bit rates from top to bottom are 0.3 bpp, 0.5 bpp, and 0.8 bpp, respectively. Compared with 2.0 bpp; it can be seen from the comparison of FIG. 8 that when the image compression rate is 0.3 bpp, 0.5 bpp, and 0.8 bpp of the low bit rate, the image compressed by the proposed method, wherein the face and the periphery of the face The edge part is more obvious and clearer than the fixed ROI mask. This is because the automatically detected ROI mask can effectively extract the area of interest in the image without wasting extra compression bits on other image parts. For a high bit rate 2.0 bPP image, the visual effects of the two compressed images are almost the same because the bit data of the two compressed images has been almost decoded. The ROI option encoding in JPEG 2000 is useful in that when the transmission bandwidth or bit rate is low, the decoder still decompresses the image in the area of interest as much as possible 'until the bandwidth is restored or the high bit is allowed When the element rate is compressed, the decoder can compress the best image quality under this condition. Therefore, this automatic detection masking mechanism, used in JPEG 2000 compression, will be able to make the best scene multi-image presentation with the given compression rate conditions. [Features and effects] The present invention provides analysis of the content of the converted coefficients in the image encoding program, automatically detects the areas of interest in the image, and takes into account the compression bit rate requirements to generate an ROI mask. Compared with other conventional techniques for forming ROI masks, it has the following advantages: 1. In the process of image compression of the mechanism proposed by the present invention, no additional manual ROI masks need to be generated manually, or the image segmentation and recognition process is pre-processed. Obtain Roi mask information, so it has the advantages of automatic detection and generation of ROI mask. 2. The automatic detection mechanism of ROI masking proposed by the present invention considers the reduction of the calculation amount and simple statistical accumulation calculation on the bit plane. Therefore, it is compared with the conventional method of obtaining ROI masking through image segmentation and recognition processing. It has the characteristics of low calculation volume. · The mechanism proposed by the present invention is to analyze the encoding process of the converted coefficients to obtain the high and low frequency information of the correlation coefficient with the image content, and to classify whether it is an area of interest. Therefore, compared with the conventional technology, for the image data The content is even more automatically adjusted. In summary, the present invention is a novel, progressive, and industrially usable person, which should meet the patent application requirements stipulated by the Chinese Patent Law. No doubt, an application for an invention patent was filed in accordance with the law. The patent is a prayer. However, the above is only a preferred embodiment of the present invention, and is not intended to limit the scope of implementation of the present invention. For example, the shapes, structures, features, and characteristics described in the patent application scope of the present invention 23 1226189 Equal changes and modifications should be included in the scope of the patent application of the present invention. [Simplified description of the drawing] The first picture: it is a diagram of the basic steps of the JPEG shadow reduction standard of Xi Chuyi; the second picture: It is a block diagram of the image compression processing flow of JPEG 2000, which is a conventional technique. The third diagram is a flowchart of a preferred embodiment of the present invention. The fourth diagram is one of the present invention. The block diagram of the automatic detection R01 masking mechanism of the preferred embodiment, the fifth figure: it is a unit block of the EBCOT encoding process according to a preferred embodiment of the present invention, with 4 pixels as Scanning unit, one stripe at a time to complete the scanning of each 64x64 pixel coding block diagram; Figure 6: The R01 mask generation mechanism proposed by the present invention and the entire JPEG 2000 compression process relationship diagram; the seventh Λ diagram : This is the hair One of the preferred embodiments is the original image map; the seventh B picture: its reliance on the Ming-the original example of the hybrid axis original PEG 2_ compression and decompression schematic diagram; the seventh c picture: it is based on A preferred embodiment of the invention is a schematic diagram of decompression when a fixed rectangular Roi mask area is 1/3 of the image size; FIG. 7D is a white line of a preferred embodiment of the present invention The surrounding area is a schematic diagram of the automatic detection ® iR01 mask proposed by the present invention; Figure 7E: It is obtained by encoding and decoding the R01 mask in Figure 7D, which is a preferred embodiment of the present invention. Schematic diagram of the image; Figure 8A: It is a schematic diagram of a compressed rectangular RoI mask covering a preferred embodiment of the present invention; and Figure 8B: It is a preferred implementation of the present invention Schematic illustration of the compressed image of the automatic detection R01 masking method. □ Continued pages (when the invention description page is insufficient, please note and use the continued pages)

Claims (1)

1226189 Ο- -iui 'Zf * S. ea f〇It ^ Year 苋 Ell application for remodeling. 1 · A method for automatically detecting areas of interest in an image, which analyzes the content of the coefficients of the image after conversion in the image compression standard, and applies In order to automatically detect the area of interest and generate a ROI mask during the compression process, the main steps include: a. Analyze the frequency change of the coefficient of the image after conversion, and use a PXP pixel area The block is a statistical unit, and the image is classified as belonging to an area of interest and a background area; b. After obtaining the small blocks detected as the area of interest (ROI) in the image, find the distribution of the small blocks One of the center positions; and c. Applying an image processing type processing to combine the small block, and cooperate with a compressed bit rate to combine the small block center point outwards to produce a closed and continuous area of interest as much as possible (ROI) 〇2. The method for automatically detecting an area of interest in an image as described in item 1 of the scope of patent application, wherein the image is compressed by a JPEG image compression standard. 3. The method for automatically detecting the area of interest in an image as described in item 1 of the scope of the patent application, wherein the image is compressed by a JPEG 2000 image compression standard. 4 · The method for automatically detecting an area of interest in an image as described in item 1 of the scope of patent application, wherein in step a it uses the high and low frequency changes of the coefficients in the 8x8 pixel block after a discrete cosine transform, and statistics A critical threshold is calculated for the high and low frequency changes of the entire image, and determines whether each 8x8 pixel block belongs to the area of interest. 5 · The method of automatically detecting an area of interest in an image as described in item 1 of the scope of the patent application, wherein in step a, it uses a discrete wavelet transform and uses a small unit block of 4x4 pixels as the statistical unit. The number of significant bits in a small unit block is determined according to the significance of the coefficients in the bit plane to determine the relevant threshold 値 to determine whether each small unit block belongs to the area of interest. 6 · The method for automatically detecting an area of interest in an image as described in item 1 of the scope of patent application, wherein in step a, it uses an image compression method based on coefficient conversion and uses ρχρ 25 1226189 f 7 „to change pages Among the small unit areas of pixels, pxp is a suitable small block size for exploring the coefficients of high and low frequency distributions after conversion. Count the high and low frequency changes in each small unit block to determine whether each small unit block belongs to the area of interest. 7 · The method for automatically detecting an area of interest in an image as described in item 1 of the scope of the patent application, wherein in step b, the content of the converted coefficients, which belongs to the pxp small unit block set of the area of interest, is discussed by The coordinates of the top, bottom, left, and right small blocks of the image are distributed, and the weight of the relevant small unit block set is selected to quickly calculate the center point position of the small block set. Among them, pxp can be viewed in various image compressions. The size of the conversion block may be convenient for analyzing the converted coefficients. 8 · Automatic detection of areas of interest in the image as described in item 1 of the scope of patent application Method, wherein in step c, the type processing in an image processing is used, and an operation mask is used to cooperate with an invasion and an expansion operation on the small block set, and cooperate with the compression bit Requirements, and try to make the combined areas appear closed and continuous areas, and finally get an ROI mask with an area related to the image content and bit rate requirements. 9 · A method to automatically detect the area of interest in the image, The method is to analyze the content of the coefficients after the image conversion in the image compression standard, and the application is to automatically detect the area of interest and generate a ROI mask during the compression process. The main steps include: a. Analysis of the frequency and position of the transformed coefficients to classify small unit blocks; b. Find the center point of the set of small units of interest that has been determined to be of interest in the image; and c. Generate an area of interest (ROI) mask. 10 · The method for automatically detecting an area of interest in an image as described in item 9 of the scope of patent application, wherein the image is compressed by a JPEG image compression standard. 11 · As applied A method for automatically detecting an area of interest in an image described in item 9 of the patent scope, wherein the image is compressed by an JPEG 2000 image compression standard. 12 · An automatic detection image as described in item 9 of the patent scope The method of the area of interest ', wherein in step a, it uses the high 26 26 26 189 low frequency change of the coefficient in a discrete 8x8 pixel block, and calculates the high and low frequency changes of the entire image to obtain a critical threshold. Determine whether each 8x8 pixel block belongs to the area of interest. U. The method of automatically detecting the area of interest in the image as described in item 9 of the scope of patent application, wherein in step a, it uses a discrete wavelet transform to A small unit block of 4x4 pixels is a statistical unit. It counts the number of significant bits in each small unit block, and obtains the relevant critical threshold according to the meaning of the coefficient in the bit plane. Each small unit block is determined. Whether it belongs to the area of interest. 14 · The method for automatically detecting an area of interest in an image as described in item 9 of the scope of the patent application, wherein in step a, it uses an image compression method based on coefficient conversion, in small unit blocks of pxp pixels It is a statistical unit, in which pxp is a suitable small block size for exploring the coefficient of high-low frequency distribution after conversion. It counts the high-low frequency changes in each small unit block, and determines whether each small unit block belongs to the area of interest. 15 · The method for automatically detecting an area of interest in an image as described in item 9 of the scope of the patent application, wherein in step b, the content of the converted coefficients and belonging to the ρχρ small unit block set of the area of interest are discussed by The coordinates of the top, bottom, leftmost, and rightmost small blocks distributed in the image are used to select the weight of the relevant small unit block set to quickly calculate the center point position of the small block set. Among them, ρχρ can be seen in various image compressions. Depending on the size of the conversion block, it is convenient to analyze the converted coefficients. 16. The method for automatically detecting an area of interest in an image as described in item 9 of the scope of the patent application, wherein in step c, the type processing in an image processing is used, and a calculation mask is used to mask the counterpart < The small G block set cooperates with an erosion and dilation, and meets the requirements of compression bit rate, and tries to make the combined area appear as a closed and continuous area. Finally, the area and image content and bit are obtained. The rate requires the associated ROI mask. □ Continued pages (Please note and use continuation pages if the patent application page is insufficient.) 1226189
The fourth picture 1226189 repair f i1 day
The statistical unit is taken as a 4 * 4 small unit block.
Preprocessing-► Discrete Wavelet Transformation-► Quantization
Transmission Status ROI Mask Generator Picture 6
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TWI420906B (en) * 2010-10-13 2013-12-21 Ind Tech Res Inst Tracking system and method for regions of interest and computer program product thereof

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TWI333169B (en) 2007-07-06 2010-11-11 Quanta Comp Inc Image recognition method and image recognition apparatus
JP5366304B2 (en) * 2009-05-19 2013-12-11 ルネサスエレクトロニクス株式会社 Display driving apparatus and operation method thereof
US9514531B2 (en) 2012-02-02 2016-12-06 Hitachi, Ltd. Medical image diagnostic device and method for setting region of interest therefor

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* Cited by examiner, † Cited by third party
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TWI420906B (en) * 2010-10-13 2013-12-21 Ind Tech Res Inst Tracking system and method for regions of interest and computer program product thereof

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