• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    An Efficient HW/SW Design for Text Extraction from Complex Color Image

    2022-08-23 02:21:52MohamedAminBenAtitallahRostomKachouriAhmedBenAtitallahandHasseneMnif
    Computers Materials&Continua 2022年6期

    Mohamed Amin Ben Atitallah,Rostom Kachouri,Ahmed Ben Atitallah and Hassene Mnif

    1LETI(E.N.I.S.),University of Sfax,Sfax,Tunisia

    2Laboratory of informatics,Gaspard-Monge,A3SI,ESIEE Paris,CNRS,Gustave Eiffel University,France

    3National Engineering School of Gabes(ENIG),University of Gabes,Tunisia

    4Department of Electrical Engineering,Jouf University,Sakaka,Aljouf,Saudi Arabia

    Abstract:In the context of constructing an embedded system to help visually impaired people to interpret text,in this paper,an efficient High-level synthesis(HLS) Hardware/Software (HW/SW) design for text extraction using the Gamma Correction Method (GCM) is proposed.Indeed, the GCM is a common method used to extract text from a complex color image and video.The purpose of this work is to study the complexity of the GCM method on Xilinx ZCU102 FPGA board and to propose a HW implementation as Intellectual Property (IP) block of the critical blocks in this method using HLS flow with taking account the quality of the text extraction.This IP is integrated and connected to the ARM Cortex-A53 as coprocessor in HW/SW codesign context.The experimental results show that the HLS HW/SW implementation of the GCM method on ZCU102 FPGA board allows a reduction in processing time by about 89%compared to the SW implementation.This result is given for the same potency and strength of SW implementation for the text extraction.

    Keywords:Text extraction;GCM;HW/SW codesign;FPGA;HLS flow

    1 Introduction

    With reference to the World Health Organization,around of the 1.3 billion people live in the world with a few forms of vision impairment[1].Thus,constructing a personal text-to-speech system aids them to interpret text.In this context extracting text from image or video presents play an important role to develop such system that helps the society and people.For that a robust and accurate algorithm should be selected for text detection.This algorithm should categorize and retrieve all text from multimedia documents in real-time.Otherwise, it can be a vital drawback to extract and recognize the text characters due to their font style, alignment, orientation, textured background, complex colored,etc.

    In literature, several methods [2–5] are proposed for text extraction from complex image.These methods use wavelet transform,thresholding,histogram technique,etc.The most important method developed for text extraction is the Gamma Correction Method (GCM) [6].In fact, the GCM method outperforms the existing methods by suppress completely the background while conserving the features of the text[7].In this method the Gray-Level Co-occurrence Matrix(GLCM)is calculated to determine the energy and the contrast.These textural features are used to characterize each gamma modified image and extract text from image.Unfortunately,the GCM method provides a high computationally complexity which presents a big issue in computer vision specially for the applications that need a real-time text extraction [8] such as text-to-speech system for helping the blind persons.However, many works in literature are concentrated on accelerating the proceedings of the GCM method by using software(SW)optimization or/and hardware(HW)implementation.

    Indeed, Kachouri et al.[8] proposed an accelerated GCM (AGCM) which presents a software optimization of the GCM method.The AGCM proposes a sub-range of values where the chosen gamma should be found per the GCM method.But, the AGCM can affect the quality of the text extraction because the text features are determined from some gamma modified image only.Girisha et al.[9] proposed a Field Programmable Gate Array (FPGA) implementation of the GLCM for 8 × 8 image size.Each image pixel is presented by 4 bits.So, in this work, a 16 × 16 GLCM is computed for only a single direction (θ = 0?).Akoushideh et al.[10] presented a HW architecture to implement four 256×256 GLCM matrix for four θ angles(0?,45?,90?and 135?)and their texture features.These matrixes are computed in parallel to reduce the execution time.But this architecture can support only 128 × 128 image size.Besides, the calculation of the texture features is realized based fixed-point operation which can generate an inaccurate result.Maroulis et al.[11] proposed a HW design to calculate sixteen 64×64 GLCMs in parallel and four texture features which are the entropy using a single core, the inverse difference moment, the correlation and the angular second moment.The inconvenient of this design that it uses the fixed-point operations to calculate the texture features instead of the floating-point operations.Further,it does not take in consideration the overlap between the diverse blocks of the image.Boudabous et al.[12]developed two hardware architectures to compute the contrast and energy texture features for GCM method.These architectures are integrated as custom instruction (CI) to the NIOS II softcore processor.Nevertheless, the performance of the developed system is far to work in real-time.

    Recently, through the technology advances in system integration based on FPGA, we can see the system design as some functional block with at least one processor as the processing unit.The general idea is to process the complex computing tasks by the hardware part which allows to exploit the pipeline and the parallelism in algorithms,and to operate the software flexibility,resulting in a context of HW/SW codesign[13–15].Thus,with increasing in the complexity of the FPGA design,it is needed to elevate the design space abstraction level from traditional approach based on Register-Transfer Level(RTL)to an effective approach allowed to decrease the FPGA design complexity.Therefore,in this day,the high-level approach based on software programming language such as C,C++,SystemC is became widely used with FPGA [16–18].This permits to increase the designer productivity and reduce the run-time in the design flow.

    However, the goal of our work is to study and implement in the HW/SW codesign context the GCM method.This method is used for text extraction from a complex color image.In fact,the HLS flow is used to design and implement the complex parts of the GCM method as an intellectual property(IPs)block.These blocks are connected as hardware coprocessor to the hardcore ARM Cortex-A53 and implemented on Xilinx ZCU102 FPGA board in a HW/SW codesign context to increase the reliability and the efficiency of the GCM method for text extraction.

    Therefore, this paper presents in Section II the overview of GCM method.In Section III, the complexity study of the GCM method on ZCU102 FPGA board is detailed.In Section IV,the HLS implementation of the complex blocks in the GCM method is described.Section V details the HW/SW implementation of the GCM method.The performance evaluation of the proposed HW/SW GCM design is the subject of Section VI.In Section VII,conclusions are drawn.

    2 GCM Overview

    The GCM [7] is an important method to extract text from a complex color image by removing the background and extracting the text.Indeed,the GCM method compute from the input image one hundred modified images by variation theγvalue from 0.1 to 10.0 using 0.1 as increment step.In fact,as reported in Fig.1,the pixel intensity of the modified image is depended on the gamma(γ)value.So ifγ >1 thus the gamma modified image becomes darker,but ifγ <1 thus the modified image becomes lighter than the original image.

    Figure 1:Gamma modified image

    The GCM method calculates for each gamma modified image (γvalue varies from 0.1 to 10.0 using 0.1 as increment step)four GLCM matrix,the textural features(energy and contrast)and the threshold value to extract the text from the image.Indeed, four GLCM for a given θ angle (θ = 0?,45?,90?,135?)are computed to determine the textural features.In fact,Haralick et al.[19]proposed the GLCM as a second-order histogram statistic.However, the GLCM represents a best algorithm used for the texture analysis.It is calculated through counting all pairs of pixels in a gray level image having (i, j) coordinate for a specified distance (d) and angle (θ).Nevertheless, θ is included in the value range(θ=0?,45?,90?,135?).For each(i,j)pixel in the image,the direction of the four θ angles is determined as presented in Fig.2.The GCM uses only the energy(Eq.(1))and the contrast(Eq.(2))as texture features from the 14 statistical GLCM features[20]proposed by Haralick et al.The energy describes the smoothness of the image.But the contrast indicates the spatial frequency of the image and a diverse moment of the GLCM.On the other hand, the image threshold value for the gamma modified image is calculated based on Otsu’s method[21]which looks for the adequate threshold that decreases the variance within the class defined, as illustrated by Eq.(3), as a weighted addition of variance of the 2 classes.However,this method assumes that the threshold image holds 2 pixels classes(e.g:foreground and background).After,it computes the ideal threshold dissociating these 2 classes where their combined propagation is minimal.

    Figure 2:Four GLCM θ angle

    where Ng is the possible number of gray level value,p(i,j)is the GLCM value,R is a normalization term andαis an angle value.

    where σ1,σ2correspond to the inter-class variance.ωcorresponds to the probability to be in class one or class two.

    Therefore, as detailed in Fig.3, the GCM applies three rules based on the energy, contrast and threshold to extract the best value of gamma with which it is possible to suppress from the modified image the largest amount of the background and generate the binarized image with the text features only.

    Tab.1 illustrates an example for computing the best gamma value for a test image presented in Fig.4 (x) which selected from ICDAR Dataset [22].In the beginning all gamma values have the same probability to be nominated per the GCM method for generating the optimal gamma for text extraction.However, to determine the best gamma for the image in Fig.4, the rules in Fig.3 are applied.Hence, the rule 3 is selected because for theγ= 1, the contrast is less than 1000 and the energy is less than 0.05.In this case,the best gamma value generated to extract the text from the image is equal to 5.5 as depicted in Fig.4(y)and Tab.1.But the best threshold(T)selected to remove the background and convert the gray level image to the binary image is equal to 0.292969 as shown in Fig.4(z)and Tab.1.

    Figure 3:The GCM rules

    Table 1: Example of computing values for test image in Fig.4

    Table 1:Continued

    Figure 4: (x) Original image “Contrast = 336.098, Energy = 0.00111243”(y) Modified image “γ =5.5”(z)Binarized image“T=0.292969”

    3 Complexity Study of the GCM Method

    For the GCM complexity study,the ZCU102 FPGA board[23]is used.This board is based on the Zynq UltraScale+XCZU9EG Xilinx FPGA which contains quad-core ARM Cortex-A53 processor working at 677 MHz.The GCM C code is developed and executed in standalone mode under the ARM Cortex-A53 using the Xilinx Software Development Kit(SDK).In this mode,it should enable the “ff.h” library to manage the writing and the reading of the files from SD card.The execution time of the GCM method is given for the average of several images of size 256 × 256 pixels from ICDAR Dataset.This time is equal to 8.25 s which cannot be suitable for a real-time application.Therefore, it seems necessary to study the distribution of the CPU time according to the different processing constituting the GCM method in order to design a real-time system for text extraction from complex color image in HW/SW codesign context.The Fig.5 gives a breakdown of the CPU time of the different blocks of the GCM method.

    From Fig.5,we can see that the complexity is highly localized in three processing blocks(GLCM,contrast and energy).Indeed, the GLCM needs 44% of CPU time.But the contrast and energy require 25%and 24%of CPU time,respectively.Thus,these results help us to make a hypothesis on the hardware and software distribution of the treatments: blocks which must be totally or partially implemented in HW and blocks likely to be implemented in software.In fact,the GLCM block must be fully implemented in HW because of its memory intensive access.Besides, we can profit by the parallelism provided by the HW to compute the four GLCM matrix for θ=0?,45?,90?and 135?and the textural features(contrast and energy)associated with each GLCM matrix in parallel.However,the contrast and energy are calculated based on the GLCM matrix using Eqs.(1)and(2),respectively.The results are obtained by these equations are in floating-point which cannot be implemented in HW.Therefore, to solve this problem, the sum operations in the contrast and energy equations are performed in HW for reducing the time needed to access to the values of the GLCM matrix.But the division by the parameterRis carried out in SW in order to not lost in the accuracy of the results given by the contrast and energy.Further, the computation of the one hundred modified images is realized by using floating-point operation.For that,the SW implementation is preferred to perform the gamma estimation algorithm by variation theγvalue from 0.1 to 10.0 using 0.1 as increment step.On the other hand,the otsu algorithm is based on several conditions.For this,we believe that the HW implementation cannot accelerate this algorithm.Accordingly,we suggest to implement it in SW.

    Figure 5:The average CPU execution time of different blocks of the GCM method

    4 HLS Architecture of the GLCM Coprocessor

    Since their creation, the FPGAs have not stopped evolving.It is used to implement several applications in various field such as image and video processing [24,25], IoT system [26], neural networks[27],etc.Recently,the High-level synthesis(HLS)flow is used with FPGA to decrease the design cycle time and increase the flexibility and the productivity of design by performing the design space exploration from a given behavioural description based on a high-level programming language(Like:SystemC,C/C++,etc.)without having to actually adapt it.For that,the HLS is emerged as an efficient and powerful tool to speed up the process of designing a hardware architecture.In this context,several HLS tools are elaborated like Xilinx Vivado HLS tool which permits through the directives to control how the design can be implemented, e.g., the arrays can be implemented as registers or memories,the loops can be pipelined or not unrolled or fully/partially unrolled.Thus,this tool allows in fraction of time to explore the design space and generate an optimized hardware design with taken an account a trade-off between hardware cost and execution time.

    Thereby, in this work, the GLCM coprocessor is designed using the Vivado HLS 18.1 tool as IP block.However,as reported in Fig.6,the GLCM coprocessor collects the image pixels through a circular buffer and computes the GLCM matrix for four θ angles(θ=0?,45?,90?,135?)with distance(d=1)between two pixels and the textural features(contrast and energy)associated for each θ angle.In fact, the developed coprocessor contains two inputs: vector 1 and vector 2.Indeed, the vector 1 contains a line of image.But the vector 2 contains the next line.8 bits are used to store pixel in each vectors.Consequently,the Ng is equal to 256 and the size of the GLCM matrix is 256×256 coefficients.The GLCM coprocessor receives in parallel two pixels from vector 1 and 2.Each pixel received is stored in internal circular buffer to reduce the DDR external access and increase the data bandwidth.Fig.7 illustrates the principle of the circular buffer unit which contains the centrale pixel(black.background)and its neighboring pixel(gray.background)for the four θ angles(θ=0?,45?,90?,135?).Once,the five pixels are ready Fig.7A,the central pixel and the neighboring pixel for a specific direction are sent to calculate the associate GLCM matrix in parallel.After that,as illustrated in Fig.7B,the central pixel is glided by one pixel to the right and a new neighboring pixel are introduced into the circular buffer unit.Once the four GLCM matrix are calculated, the computation of the preliminary values of the contrast and energy are started in parallel.In the end,the GLCM coprocessor provides as output two values used to calculate the contrast and the energy.

    Figure 6:HLS hardware design of the GLCM coprocessor

    The C programming language is used to develop the specification of the GLCM coprocessor.The instruction in the C code is written with Vivado HLS friendly syntax.The optimization steps are applied to the C code by adding incrementally several directives.Thus,three solutions are generated to implement the GLCM coprocessor.Tab.2 and Fig.8 illustrate the comparison of these solutions in terms of hardware cost and the number of clock cycles,respectively.

    However,the solution 1 are generated with Vivado HLS 18.1 tool without adding any directives.According to Tab.2,this solution consumes 1%of Look-up-Table(LUT),0.3%of Flip-Flop(FF),13%of BRAM blocks and 0.3% of DSP blocks of the Zynq XCZU9EG Xilinx FPGA.But the number of clock cycles is equal to 459324, as reported in Fig.8 to compute four GLCM matrix and the preliminary values of the contrast and energy for 256×256 pixels image size.To decrease the number of clock cycles,the solution 2 is generated by applying the PIPLINE directive in the loop iterations.The PIPLINE is used with an interval equal to 1 to reduce the time latency.This solution allows to decrease the number of clock cycles by 57%as depicted in Fig.8 with an increase in number of LUTs,FFs and DSP blocks by 88%,89%and 93%,respectively(Tab.2),relative to the solution 1.From this result we can see an important increase in the hardware cost for solution 2.Thus,we have to integrate the ALLOCATION and RESSOURCE directives to the GLCM C code to reduce the hardware cost.In fact,the ALLOCATION directive is added to process the multiplication operations which allows to share the hardware resources between several operations.Furthermore,the RESOURCE directive is used to implement the GLCM matrix by a specific memory blocks (BRAMs).This optimization allowed to design the solution 3 which gives a decrease in number of LUTs and FFs by 3.4% and 3%,respectively compared to solution 2 as illustrated in Tab.2 with the same number of clock cycles as proved from Fig.8.Afterward, the solution 3 is used for HW/SW implementation of the GCM method.

    Figure 7:Circular buffer unit

    Table 2: Comparison of the hardware cost for HLS solution

    Figure 8:Comparison of the number of clock cycles for HLS solutions

    5 HW/SW Implementation of the GCM Method

    The ZCU102 board is selected for the HW/SW implementation of the GCM method.In this board, the Zynq XCZU9EG FPGA consists by two parts: the Processing System (PS) part and the Programmable Logic(PL)part.The PS part is based on the ARM Cortex-A53 processor.But the PL part contains 274k LUTs,2520 DSPs block and 32.1 Mb memory.This architecture is completed by industry standard AXI(Advanced eXtensible Interface)interface protocol which provided by ARM as part of the Advanced Microcontroller Bus Architecture (AMBA) standard.The AXI interface affords a high bandwidth,low latency connection between PS and PL parts.However,there are two main AXI4-interfaces:AXI4-Stream and AXI4-Lite.In fact,the AXI4-Stream provides a high-speed streaming data by using point-to-point streaming data without indicating any addresses.But the AXI4-Lite is a traditional low throughput memory communication used for example from/to status and control registers.

    Figure 9:HLS HW/SW GCM design

    Fig.9 describes the HW/SW design to implement the GCM algorithm.In fact, the SW part is developed by C/C++programming language which is used to control the data transfer between the DDR memory and the GLCM coprocessor,to generate the gamma modified images,to compute the finale value of the energy and the contrast and to determine the threshold value through the otsu algorithm for extracting text from color image.Further,the SW part is compiled for the standalone mode using the SDK tool to generate the executable file(.elf)and performed by the PS part through the ARM Cortex-A53 processor at 677 MHz.On the other hand,the HW part is used to implement the GLCM algorithm on PL part and perform it at 100 MHz.The connection between the PS and PL parts is realized through the AXI4-Stream interface by using Direct Memory Access (DMA) to increase the data bandwidth.Thus, two DMAs (DMA1 and DMA2) are connected to the GLCM coprocessor for parallel data transfer with DDR memory.Indeed,DMA1 is configurated in write/read mode.But DMA2 is configurated in read mode only.But the AXI4-Lite is used to configure the DMA by indicating the address from where it starts to read data and the data length.These interfaces(AXI4-Stream and AXI4-Lite) are created using the Vivado HLS tool when generating the GLCM coprocessor which is exported as IP core to the Vivado design tool 18.1 to produce the HW/SW GCM design.

    Nevertheless, in the beginning, the color image is stored in the DDR memory.Then, the ARM Cortex-A53 initiates through AXI4-Lite interface the DMA to transfer two image lines in parallel from DDR memory to GLCM coprocessor using DMA1 and DMA2.Once the GLCM coprocessor receives pixels, it starts to compute in parallel the GLCM matrix for four θ angles (θ = 0?, 45?, 90?,135?) and the textural features (contrast and energy) associated for each θ angle as shown in Fig.6.When,the GLCM coprocessor finish to process data,the DMA1 send the preliminary values of the contrast and energy to the PS part where the ARM Cortex-A53 processor performs a floating-point division by the parameterRas indicated by Eqs.(1)and(2)to obtain the final values of the energy and contrast, respectively.Then, it calculates the threshold (T) using otsu algorithm and determines the optimum value of gamma which allows to eliminate the background and extract the text from complex color image.

    The HLS HW/SW GCM design is synthesized and implemented by Vivado design tool 18.1.The implementation results shows that this design uses 51308 (18.72%) of LUTs, 45206 (8.24%) of FFs,123(13.49%)of BRAMs and 118(4.68%)of DSPs blocks of the Zynq XCZU9EG FPGA.

    6 Performance Evaluation

    The performance evaluation of the HW/SW GCM design is carried out on the ZCU102 board.The performance is evaluated and compared to the GCM SW implementation in terms of the efficiency of correctly detected characters in the image and the execution time.However,in experimental evaluation,the execution time is measured by the PS timer.Besides, several images from ICDAR Dataset are selected.The size of this image is 256×256 pixels.Furthermore,the F-measure is computed based on Eq.(4)to analysis the performance of the text extraction.In fact,F-measure is the harmonic mean of recall(Eq.(5))and precision(Eq.(6))rates[28].

    From Fig.10, we can conclude that the HW/SW implementation of the GCM method reduces dramatically the execution time by 89%compared to the SW implementation.This result is provided for the same performance for text extraction which is justified by the value of the F-Measure for HW/SW and SW implementation of the GCM method as presented in Fig.11.

    Figure 10: Comparaison of execution time between SW and HW/SW implementation of the GCM method

    Figure 11: (x) Original image, (y) Text extraction with SW implemetation, (z) Text extraction with HW/SW implementation

    Tab.3 presents the comparison of our HW/SW GCM design with works proposed in literature.All works use image from ICDAR dataset to evaluate their proposition,However,we can notice that our design is more performant and can consume less energy than[8]which proposes some optimization in the GCM method to determine the best gamma value from some modified image.This affects the efficiency of the text extraction.Also, our design is more performant than [12] which add custom instruction to NIOS II processor to compute contrast and energy on hardware.

    Table 3: Comparison of the proposed GCM design

    7 Conclusion

    In this paper, the HW/SW codesign implementation of the GCM algorithm is proposed.The HLS flow is used to design an efficient HW architecture for the critical block in the GCM by adding incrementally to the C code some directives (such as PIPELINE, ALLOCATION, RESSOURCE)through Xilinx Vivado HLS tool.This block is implemented in the PL part as IP block and integrated with the ARM Cortex-A53 processor in HW/SW codesign context.The data transfer between the DDR memory and the PL part is carried out by using the AXI4-stream through the DMA to increase the data bandwidth.The evaluation of the proposed design is realized on ZCU102 FPGA board.The experimental results show that the HLS HW/SW GCM design speed up the execution time by 89%relative to the SW implementation with same value of the F-Measure for correctly text extraction.However,the designed system can be integrated into the text-to-speech system to help visually impaired people to interpret text.

    Funding Statement:The authors received no specific funding for this study.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    91av网一区二区| 97人妻精品一区二区三区麻豆| 99热这里只有是精品50| 99久久无色码亚洲精品果冻| 午夜福利18| netflix在线观看网站| 亚洲人成伊人成综合网2020| 欧美日韩乱码在线| 久久香蕉精品热| 亚洲av不卡在线观看| 欧美黑人巨大hd| 国内揄拍国产精品人妻在线| 看十八女毛片水多多多| 天堂影院成人在线观看| 精品久久久久久久末码| 成人三级黄色视频| 国产精品乱码一区二三区的特点| 中文字幕人妻熟人妻熟丝袜美| 露出奶头的视频| 色在线成人网| 午夜日韩欧美国产| 国产成人影院久久av| 亚洲美女黄片视频| 免费观看精品视频网站| bbb黄色大片| 我要搜黄色片| 日日啪夜夜撸| 亚洲中文日韩欧美视频| 99热这里只有是精品在线观看| 亚洲av成人av| 国产成人一区二区在线| 午夜激情欧美在线| 国产伦精品一区二区三区视频9| 国产精品一区二区三区四区免费观看 | 欧美最黄视频在线播放免费| a在线观看视频网站| 黄色视频,在线免费观看| 国产高潮美女av| 久久久久精品国产欧美久久久| 国产精华一区二区三区| 国产精品,欧美在线| 亚洲成人久久性| 美女被艹到高潮喷水动态| 性欧美人与动物交配| 夜夜爽天天搞| 亚洲av美国av| 免费在线观看影片大全网站| 高清在线国产一区| 精品一区二区三区视频在线| 99热只有精品国产| 在线天堂最新版资源| 欧美潮喷喷水| 久久久国产成人精品二区| 一级毛片久久久久久久久女| 一区福利在线观看| 国产精品一区二区三区四区免费观看 | 九九久久精品国产亚洲av麻豆| 亚洲av二区三区四区| 国产 一区 欧美 日韩| 日韩高清综合在线| 国产不卡一卡二| 精品一区二区三区视频在线观看免费| 国产又黄又爽又无遮挡在线| 国产成人aa在线观看| 国产一区二区三区在线臀色熟女| 日韩中字成人| 久久久久国产精品人妻aⅴ院| 真实男女啪啪啪动态图| 久久99热6这里只有精品| 男女之事视频高清在线观看| 亚洲成人中文字幕在线播放| 亚洲精华国产精华液的使用体验 | 啦啦啦观看免费观看视频高清| 中文字幕精品亚洲无线码一区| 国产一区二区亚洲精品在线观看| 99久久久亚洲精品蜜臀av| 内射极品少妇av片p| 99久久成人亚洲精品观看| 99在线视频只有这里精品首页| 99在线人妻在线中文字幕| 国产爱豆传媒在线观看| 中文字幕av在线有码专区| 欧美xxxx性猛交bbbb| 岛国在线免费视频观看| 色哟哟·www| 日本与韩国留学比较| 91狼人影院| 可以在线观看毛片的网站| 中文字幕免费在线视频6| 中国美女看黄片| 国产精品人妻久久久影院| 欧美三级亚洲精品| 精品一区二区三区av网在线观看| 亚洲国产精品成人综合色| 国产精品一区二区三区四区久久| 午夜影院日韩av| 我的老师免费观看完整版| 日本熟妇午夜| 中文在线观看免费www的网站| 18禁黄网站禁片午夜丰满| 国模一区二区三区四区视频| 狠狠狠狠99中文字幕| 欧美精品国产亚洲| 久久这里只有精品中国| 婷婷精品国产亚洲av在线| 国产乱人伦免费视频| 最新在线观看一区二区三区| 欧美日韩瑟瑟在线播放| 欧美日韩国产亚洲二区| 久久草成人影院| 国内久久婷婷六月综合欲色啪| 在线免费观看不下载黄p国产 | 亚洲人成伊人成综合网2020| 春色校园在线视频观看| 性色avwww在线观看| 久久久久国内视频| a级毛片免费高清观看在线播放| 国产一区二区三区av在线 | 最近视频中文字幕2019在线8| 香蕉av资源在线| 国产精品一区二区性色av| .国产精品久久| 午夜视频国产福利| 尤物成人国产欧美一区二区三区| 麻豆av噜噜一区二区三区| 别揉我奶头~嗯~啊~动态视频| 日本一二三区视频观看| 久久6这里有精品| 国产一区二区三区在线臀色熟女| 亚洲av免费在线观看| 国产精品av视频在线免费观看| 久久久午夜欧美精品| 人人妻,人人澡人人爽秒播| 久久精品国产亚洲av涩爱 | 国产午夜精品久久久久久一区二区三区 | 一边摸一边抽搐一进一小说| 欧美精品国产亚洲| 成人三级黄色视频| 国内少妇人妻偷人精品xxx网站| 麻豆一二三区av精品| 日本黄色片子视频| 欧美成人性av电影在线观看| 五月玫瑰六月丁香| 亚洲专区中文字幕在线| 日韩,欧美,国产一区二区三区 | 听说在线观看完整版免费高清| 亚洲中文字幕一区二区三区有码在线看| 免费av观看视频| 真人做人爱边吃奶动态| 国产乱人伦免费视频| 国产av在哪里看| 成人国产综合亚洲| 啦啦啦观看免费观看视频高清| 老司机福利观看| 午夜福利在线观看吧| 欧美黑人欧美精品刺激| 国产男靠女视频免费网站| 全区人妻精品视频| 白带黄色成豆腐渣| 少妇的逼水好多| 亚洲久久久久久中文字幕| 性欧美人与动物交配| 日韩 亚洲 欧美在线| 一个人免费在线观看电影| 婷婷精品国产亚洲av在线| 国产乱人视频| 看十八女毛片水多多多| 中文亚洲av片在线观看爽| 看免费成人av毛片| 国产成人一区二区在线| 亚洲成av人片在线播放无| 亚洲av不卡在线观看| 国产单亲对白刺激| 性欧美人与动物交配| 欧美丝袜亚洲另类 | 中文资源天堂在线| 精品久久久久久久久久免费视频| 精品99又大又爽又粗少妇毛片 | 在线a可以看的网站| av.在线天堂| 国产精品亚洲美女久久久| 可以在线观看毛片的网站| 日本撒尿小便嘘嘘汇集6| 精品一区二区三区视频在线| 狠狠狠狠99中文字幕| 精品久久久久久久久av| 精品乱码久久久久久99久播| 日韩欧美精品v在线| 波多野结衣高清无吗| 不卡一级毛片| 欧美高清成人免费视频www| 又黄又爽又刺激的免费视频.| 好男人在线观看高清免费视频| 欧美xxxx黑人xx丫x性爽| 变态另类丝袜制服| 亚洲精品国产成人久久av| 天天躁日日操中文字幕| 在线观看美女被高潮喷水网站| 九九热线精品视视频播放| 亚洲内射少妇av| 少妇的逼好多水| 亚洲18禁久久av| 美女黄网站色视频| 我的女老师完整版在线观看| 国产精品av视频在线免费观看| 国产免费av片在线观看野外av| 美女大奶头视频| 久久精品国产亚洲av香蕉五月| 日韩欧美三级三区| 亚州av有码| 色综合站精品国产| 嫩草影院精品99| 欧美性感艳星| 欧美成人性av电影在线观看| 一夜夜www| 国产黄色小视频在线观看| 欧美成人一区二区免费高清观看| 性色avwww在线观看| 熟女人妻精品中文字幕| 97超级碰碰碰精品色视频在线观看| 麻豆精品久久久久久蜜桃| 夜夜夜夜夜久久久久| 成人亚洲精品av一区二区| 色尼玛亚洲综合影院| 22中文网久久字幕| 身体一侧抽搐| 久久热精品热| 2021天堂中文幕一二区在线观| 嫩草影视91久久| 此物有八面人人有两片| 婷婷精品国产亚洲av在线| 亚洲乱码一区二区免费版| 黄片wwwwww| 国产精品免费一区二区三区在线| 精品一区二区免费观看| 国产欧美日韩精品亚洲av| 亚洲熟妇中文字幕五十中出| 99在线人妻在线中文字幕| 国产一区二区在线av高清观看| 国内毛片毛片毛片毛片毛片| 一区二区三区免费毛片| 99精品久久久久人妻精品| 亚洲成a人片在线一区二区| 欧美高清性xxxxhd video| 人妻丰满熟妇av一区二区三区| 在线播放无遮挡| 免费无遮挡裸体视频| 日韩中文字幕欧美一区二区| 国产乱人视频| 久久精品国产亚洲av香蕉五月| 我的女老师完整版在线观看| 九九热线精品视视频播放| 久久久久久久精品吃奶| 午夜激情欧美在线| 色尼玛亚洲综合影院| 国产成人a区在线观看| 国产在线男女| 一个人看的www免费观看视频| 人妻夜夜爽99麻豆av| 久久人人精品亚洲av| 深夜精品福利| 天美传媒精品一区二区| 午夜精品在线福利| 精品久久久久久成人av| 日日夜夜操网爽| 欧美激情久久久久久爽电影| 国产精品久久久久久亚洲av鲁大| 午夜a级毛片| 丰满乱子伦码专区| 天天躁日日操中文字幕| 三级毛片av免费| 别揉我奶头 嗯啊视频| 久久久久免费精品人妻一区二区| av.在线天堂| 黄片wwwwww| 亚洲最大成人av| 亚洲va在线va天堂va国产| 欧美中文日本在线观看视频| 欧美潮喷喷水| 亚洲人成网站在线播放欧美日韩| 99国产极品粉嫩在线观看| 国产精品免费一区二区三区在线| 极品教师在线免费播放| 好男人在线观看高清免费视频| 日韩欧美在线二视频| 亚洲中文日韩欧美视频| 黄色一级大片看看| ponron亚洲| 久久精品夜夜夜夜夜久久蜜豆| 亚洲无线在线观看| 亚洲最大成人手机在线| 九九爱精品视频在线观看| 亚洲性久久影院| 中文字幕av成人在线电影| 国产午夜福利久久久久久| 又粗又爽又猛毛片免费看| 久久中文看片网| 欧美成人a在线观看| 精品无人区乱码1区二区| 国产综合懂色| av天堂中文字幕网| 三级国产精品欧美在线观看| 草草在线视频免费看| 噜噜噜噜噜久久久久久91| 高清毛片免费观看视频网站| 日韩一区二区视频免费看| 搡老熟女国产l中国老女人| 国产午夜福利久久久久久| 午夜a级毛片| 亚洲av一区综合| 亚洲最大成人手机在线| 亚洲精品乱码久久久v下载方式| 中文字幕av在线有码专区| 国产视频一区二区在线看| 美女大奶头视频| 琪琪午夜伦伦电影理论片6080| 亚洲精品久久国产高清桃花| 极品教师在线视频| 国产精品不卡视频一区二区| 日本黄色视频三级网站网址| 九九久久精品国产亚洲av麻豆| 狠狠狠狠99中文字幕| aaaaa片日本免费| 国产精品电影一区二区三区| 欧美最新免费一区二区三区| 亚洲精品一区av在线观看| 国产黄a三级三级三级人| 97热精品久久久久久| 91精品国产九色| 欧美日本视频| 神马国产精品三级电影在线观看| 亚洲一级一片aⅴ在线观看| 久久久久性生活片| 欧美日韩综合久久久久久 | 18禁黄网站禁片午夜丰满| 亚洲色图av天堂| 欧美日韩中文字幕国产精品一区二区三区| 内地一区二区视频在线| 12—13女人毛片做爰片一| 欧美在线一区亚洲| 99久久精品国产国产毛片| 亚洲,欧美,日韩| 99在线人妻在线中文字幕| 欧美另类亚洲清纯唯美| 久久热精品热| 日日撸夜夜添| 日韩欧美免费精品| 国产精品嫩草影院av在线观看 | 国产黄a三级三级三级人| 亚洲av成人精品一区久久| 琪琪午夜伦伦电影理论片6080| 亚洲无线观看免费| 亚洲,欧美,日韩| 精品人妻偷拍中文字幕| 99在线人妻在线中文字幕| 亚洲av免费高清在线观看| 国产在视频线在精品| 一个人看的www免费观看视频| 成人国产麻豆网| 免费观看人在逋| 久久久国产成人精品二区| 欧美人与善性xxx| 亚洲美女黄片视频| 极品教师在线免费播放| 美女 人体艺术 gogo| 国产av麻豆久久久久久久| 12—13女人毛片做爰片一| 99在线人妻在线中文字幕| 亚洲欧美日韩高清专用| 国产色婷婷99| 精品一区二区三区视频在线观看免费| 免费不卡的大黄色大毛片视频在线观看 | videossex国产| 欧美另类亚洲清纯唯美| 婷婷精品国产亚洲av| 国产精品av视频在线免费观看| 免费搜索国产男女视频| 最新在线观看一区二区三区| av在线亚洲专区| 亚洲国产精品sss在线观看| 国产高清有码在线观看视频| 亚洲图色成人| 九九在线视频观看精品| 久久精品国产清高在天天线| 久久亚洲真实| 午夜精品一区二区三区免费看| 成年女人看的毛片在线观看| 日本精品一区二区三区蜜桃| 午夜爱爱视频在线播放| 午夜免费男女啪啪视频观看 | 午夜福利在线观看免费完整高清在 | 国产精品久久久久久av不卡| 中文在线观看免费www的网站| 国产免费一级a男人的天堂| 亚洲狠狠婷婷综合久久图片| 桃色一区二区三区在线观看| 日韩国内少妇激情av| 久久婷婷人人爽人人干人人爱| 国产亚洲精品久久久com| a级一级毛片免费在线观看| 午夜影院日韩av| 亚洲五月天丁香| 久久久精品大字幕| 国产三级中文精品| 最近中文字幕高清免费大全6 | 欧美激情久久久久久爽电影| 久久久久久伊人网av| 麻豆国产av国片精品| 赤兔流量卡办理| 午夜福利18| 性色avwww在线观看| 国产欧美日韩精品一区二区| 国产三级在线视频| 欧美zozozo另类| 观看免费一级毛片| 一进一出抽搐gif免费好疼| 亚洲无线在线观看| 又爽又黄无遮挡网站| .国产精品久久| 欧美日韩中文字幕国产精品一区二区三区| 国产日本99.免费观看| 一个人看视频在线观看www免费| 天堂动漫精品| 好男人在线观看高清免费视频| 成人精品一区二区免费| 亚洲成人中文字幕在线播放| 午夜爱爱视频在线播放| 人妻制服诱惑在线中文字幕| 一区福利在线观看| 亚洲精华国产精华精| 中文字幕久久专区| 日韩精品青青久久久久久| 欧美日韩乱码在线| 内地一区二区视频在线| 精品不卡国产一区二区三区| 亚洲18禁久久av| 午夜爱爱视频在线播放| 日韩欧美在线乱码| 亚洲成人精品中文字幕电影| 天美传媒精品一区二区| 国产久久久一区二区三区| av.在线天堂| 嫩草影院精品99| 成熟少妇高潮喷水视频| 午夜福利在线观看免费完整高清在 | 欧美性感艳星| 日本三级黄在线观看| 国产精品一区二区性色av| 国产精品精品国产色婷婷| 国产不卡一卡二| 观看美女的网站| 精品久久久久久久久亚洲 | 韩国av一区二区三区四区| 国产精品av视频在线免费观看| 国产真实乱freesex| 黄色配什么色好看| 哪里可以看免费的av片| 国产v大片淫在线免费观看| 99久久九九国产精品国产免费| 五月玫瑰六月丁香| 美女高潮的动态| 精品一区二区三区视频在线观看免费| 综合色av麻豆| 中文在线观看免费www的网站| 婷婷精品国产亚洲av在线| 国模一区二区三区四区视频| 日日夜夜操网爽| 久久精品久久久久久噜噜老黄 | 午夜福利在线观看吧| 久久精品国产鲁丝片午夜精品 | 国产亚洲av嫩草精品影院| 国产精品亚洲美女久久久| 国产亚洲91精品色在线| 欧美成人免费av一区二区三区| 欧美三级亚洲精品| 免费高清视频大片| 午夜福利成人在线免费观看| 免费在线观看影片大全网站| 啪啪无遮挡十八禁网站| 波多野结衣巨乳人妻| 久久久久久久久大av| 亚洲一区高清亚洲精品| 国产精品一区二区性色av| 丝袜美腿在线中文| 联通29元200g的流量卡| 热99在线观看视频| 精品无人区乱码1区二区| 欧美日韩综合久久久久久 | xxxwww97欧美| 国内精品美女久久久久久| 两个人视频免费观看高清| 成人一区二区视频在线观看| 久久亚洲精品不卡| 久久99热6这里只有精品| 三级国产精品欧美在线观看| 色播亚洲综合网| 韩国av一区二区三区四区| 日本精品一区二区三区蜜桃| 色av中文字幕| 男人舔奶头视频| 日韩欧美精品免费久久| 两性午夜刺激爽爽歪歪视频在线观看| 国产成人福利小说| 国产大屁股一区二区在线视频| 美女免费视频网站| 免费在线观看成人毛片| 久久精品91蜜桃| 我要搜黄色片| netflix在线观看网站| 亚洲内射少妇av| 中文在线观看免费www的网站| 欧美日韩精品成人综合77777| 在线免费观看不下载黄p国产 | 嫩草影院入口| 搡老熟女国产l中国老女人| АⅤ资源中文在线天堂| 亚洲天堂国产精品一区在线| 国产不卡一卡二| 在线免费观看不下载黄p国产 | 欧美性猛交╳xxx乱大交人| 又爽又黄无遮挡网站| 中文字幕av成人在线电影| 最近中文字幕高清免费大全6 | 亚洲va日本ⅴa欧美va伊人久久| 亚洲av日韩精品久久久久久密| 国产精品一区二区免费欧美| 国产一区二区在线观看日韩| 麻豆av噜噜一区二区三区| 欧美+亚洲+日韩+国产| 午夜精品久久久久久毛片777| 国产视频一区二区在线看| 99国产精品一区二区蜜桃av| 久久久久久久久久成人| or卡值多少钱| 国产精品电影一区二区三区| 美女高潮喷水抽搐中文字幕| 久久久久久久久久久丰满 | 亚洲狠狠婷婷综合久久图片| 美女被艹到高潮喷水动态| 久久精品影院6| 国产高清三级在线| 国产伦精品一区二区三区视频9| 蜜桃亚洲精品一区二区三区| 亚洲美女黄片视频| 国内精品久久久久久久电影| 日韩人妻高清精品专区| 人妻少妇偷人精品九色| 两个人的视频大全免费| 久久久色成人| 麻豆久久精品国产亚洲av| 国产亚洲av嫩草精品影院| 麻豆成人av在线观看| 精品久久久久久久末码| 亚洲欧美日韩东京热| 精品免费久久久久久久清纯| 国产伦一二天堂av在线观看| or卡值多少钱| 亚洲精华国产精华液的使用体验 | 成人一区二区视频在线观看| 亚洲18禁久久av| 亚洲人成网站高清观看| 我的女老师完整版在线观看| 乱码一卡2卡4卡精品| 国产精品人妻久久久影院| 欧美最黄视频在线播放免费| 国产精品一区二区三区四区久久| 久久久久久久亚洲中文字幕| 国产成年人精品一区二区| 天堂网av新在线| 久久精品影院6| 国产精品电影一区二区三区| 亚洲精品粉嫩美女一区| 窝窝影院91人妻| 欧美性猛交黑人性爽| 91在线观看av| 欧美性感艳星| 两性午夜刺激爽爽歪歪视频在线观看| 午夜亚洲福利在线播放| 亚洲最大成人中文| 久久亚洲真实| 麻豆成人av在线观看| 亚洲va在线va天堂va国产| 国产精品久久久久久久电影| 99久久精品国产国产毛片| 三级国产精品欧美在线观看| 1024手机看黄色片| 欧美一区二区精品小视频在线| 成人性生交大片免费视频hd| 22中文网久久字幕| 久久精品国产亚洲网站| 无遮挡黄片免费观看| 国产精品久久久久久久久免| 男女下面进入的视频免费午夜| 亚洲国产精品久久男人天堂| 黄色女人牲交| 观看免费一级毛片| АⅤ资源中文在线天堂| 国产激情偷乱视频一区二区| 欧美性猛交黑人性爽| 亚洲性久久影院| 91久久精品国产一区二区成人| 亚洲经典国产精华液单| 性色avwww在线观看| 亚洲中文字幕一区二区三区有码在线看| 毛片女人毛片| 日本-黄色视频高清免费观看| 欧美色视频一区免费| 日韩人妻高清精品专区| 97超级碰碰碰精品色视频在线观看| 村上凉子中文字幕在线| 欧美成人免费av一区二区三区| 亚洲成人中文字幕在线播放| 亚洲avbb在线观看| 亚洲精品色激情综合| 亚洲午夜理论影院|