• <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.

    亚洲精品成人久久久久久| 精品国产超薄肉色丝袜足j| 少妇人妻精品综合一区二区 | 九色成人免费人妻av| 久久久久久人人人人人| 9191精品国产免费久久| 成人18禁在线播放| 1024手机看黄色片| 淫秽高清视频在线观看| av国产免费在线观看| 一区二区三区激情视频| 色综合婷婷激情| 亚洲国产精品久久男人天堂| 给我免费播放毛片高清在线观看| 国产精品一区二区三区四区久久| 悠悠久久av| 欧美乱色亚洲激情| 蜜桃久久精品国产亚洲av| 黄片大片在线免费观看| 黄色视频,在线免费观看| 一a级毛片在线观看| 999久久久精品免费观看国产| 在线播放无遮挡| 人妻丰满熟妇av一区二区三区| 真实男女啪啪啪动态图| 天堂影院成人在线观看| 精品久久久久久成人av| 亚洲第一欧美日韩一区二区三区| 在线观看免费午夜福利视频| 在线观看午夜福利视频| 欧美激情在线99| 综合色av麻豆| 欧美一区二区国产精品久久精品| 久久香蕉国产精品| 中文字幕熟女人妻在线| 在线免费观看的www视频| 国产精品免费一区二区三区在线| 精品国产三级普通话版| 国产三级在线视频| av天堂在线播放| 99久久久亚洲精品蜜臀av| 欧美日韩综合久久久久久 | 男人舔奶头视频| 欧美区成人在线视频| 国产免费男女视频| 亚洲成av人片在线播放无| 成人欧美大片| xxx96com| 中出人妻视频一区二区| 国产精华一区二区三区| 听说在线观看完整版免费高清| 国产真人三级小视频在线观看| 好男人电影高清在线观看| 免费无遮挡裸体视频| 免费人成在线观看视频色| 亚洲在线自拍视频| av黄色大香蕉| 成熟少妇高潮喷水视频| 少妇的丰满在线观看| 久久久久九九精品影院| 色精品久久人妻99蜜桃| 性色av乱码一区二区三区2| 国产一区二区三区在线臀色熟女| 国产91精品成人一区二区三区| 91字幕亚洲| www日本在线高清视频| 啦啦啦韩国在线观看视频| svipshipincom国产片| 特大巨黑吊av在线直播| 日韩欧美在线二视频| 熟女少妇亚洲综合色aaa.| 亚洲人成伊人成综合网2020| 免费看美女性在线毛片视频| 香蕉av资源在线| 成人高潮视频无遮挡免费网站| 亚洲av五月六月丁香网| 一区二区三区国产精品乱码| 国产精品 欧美亚洲| 国产精品美女特级片免费视频播放器| 老鸭窝网址在线观看| 亚洲av电影在线进入| 久久久久久久久大av| 伊人久久大香线蕉亚洲五| 999久久久精品免费观看国产| 国产视频一区二区在线看| 久久精品91蜜桃| 熟妇人妻久久中文字幕3abv| 午夜免费男女啪啪视频观看 | 很黄的视频免费| 亚洲成av人片免费观看| 一本一本综合久久| 99国产精品一区二区蜜桃av| 两个人视频免费观看高清| 男女之事视频高清在线观看| 一夜夜www| 免费搜索国产男女视频| 综合色av麻豆| 亚洲激情在线av| 97人妻精品一区二区三区麻豆| 男人舔奶头视频| 精品电影一区二区在线| 三级国产精品欧美在线观看| 少妇高潮的动态图| 成人永久免费在线观看视频| 日日摸夜夜添夜夜添小说| 精品99又大又爽又粗少妇毛片 | 午夜日韩欧美国产| 成人18禁在线播放| 男女那种视频在线观看| 免费观看精品视频网站| 看片在线看免费视频| 国产精品国产高清国产av| 免费av观看视频| 亚洲国产欧美网| 丁香六月欧美| 国产精品野战在线观看| 在线观看舔阴道视频| 日韩精品中文字幕看吧| 欧美+日韩+精品| 久久久久免费精品人妻一区二区| 久久精品亚洲精品国产色婷小说| 看黄色毛片网站| 看黄色毛片网站| 成人一区二区视频在线观看| 啦啦啦观看免费观看视频高清| 国产成人a区在线观看| 成人欧美大片| 亚洲无线观看免费| 日韩欧美在线乱码| 小蜜桃在线观看免费完整版高清| 国产av麻豆久久久久久久| 夜夜躁狠狠躁天天躁| 国产精品亚洲美女久久久| 久久精品国产综合久久久| 两个人看的免费小视频| 中文字幕人妻熟人妻熟丝袜美 | 又紧又爽又黄一区二区| 国语自产精品视频在线第100页| 网址你懂的国产日韩在线| h日本视频在线播放| 亚洲欧美日韩东京热| 757午夜福利合集在线观看| 欧美日韩国产亚洲二区| 99久久99久久久精品蜜桃| 精品久久久久久久人妻蜜臀av| 美女被艹到高潮喷水动态| 最新美女视频免费是黄的| 可以在线观看毛片的网站| 国产在线精品亚洲第一网站| 天堂网av新在线| 精品国内亚洲2022精品成人| 午夜免费男女啪啪视频观看 | 97超级碰碰碰精品色视频在线观看| 在线观看免费午夜福利视频| 一级作爱视频免费观看| 免费在线观看成人毛片| 一卡2卡三卡四卡精品乱码亚洲| 久久伊人香网站| 日本在线视频免费播放| 色尼玛亚洲综合影院| 熟女人妻精品中文字幕| 欧美日韩福利视频一区二区| 51国产日韩欧美| 最新在线观看一区二区三区| 成熟少妇高潮喷水视频| 最新在线观看一区二区三区| 狂野欧美白嫩少妇大欣赏| 亚洲欧美激情综合另类| 老司机福利观看| 亚洲欧美日韩高清在线视频| 亚洲av中文字字幕乱码综合| 每晚都被弄得嗷嗷叫到高潮| 久久欧美精品欧美久久欧美| 久久婷婷人人爽人人干人人爱| 天堂√8在线中文| 免费观看人在逋| 欧美成人一区二区免费高清观看| 在线观看日韩欧美| 少妇人妻精品综合一区二区 | 久久久精品大字幕| 一级毛片高清免费大全| 国产免费男女视频| 日本黄色视频三级网站网址| 每晚都被弄得嗷嗷叫到高潮| 日韩免费av在线播放| 午夜福利在线在线| 国产真人三级小视频在线观看| 岛国视频午夜一区免费看| 国产一区二区在线av高清观看| 精品免费久久久久久久清纯| 国模一区二区三区四区视频| 1024手机看黄色片| 最新在线观看一区二区三区| 99热精品在线国产| 久久久久国产精品人妻aⅴ院| 一个人看的www免费观看视频| 成人国产综合亚洲| 精品不卡国产一区二区三区| av在线蜜桃| 亚洲精品一区av在线观看| 国产伦精品一区二区三区视频9 | 岛国在线免费视频观看| 久久久久九九精品影院| 免费在线观看成人毛片| 99久久成人亚洲精品观看| 亚洲在线观看片| 人人妻人人看人人澡| 欧美日韩瑟瑟在线播放| 国产老妇女一区| 久久欧美精品欧美久久欧美| 亚洲美女视频黄频| 国产一区二区亚洲精品在线观看| 欧美成人一区二区免费高清观看| 男女那种视频在线观看| 麻豆久久精品国产亚洲av| 18禁黄网站禁片免费观看直播| 国产精品 国内视频| 午夜久久久久精精品| 亚洲专区国产一区二区| 91av网一区二区| 天堂√8在线中文| 国产成人影院久久av| 国产综合懂色| 精品一区二区三区视频在线观看免费| 19禁男女啪啪无遮挡网站| 操出白浆在线播放| a级一级毛片免费在线观看| 亚洲电影在线观看av| 嫩草影视91久久| 中文字幕熟女人妻在线| 亚洲人成网站在线播| 国产亚洲精品久久久久久毛片| 中文字幕人妻丝袜一区二区| 少妇人妻精品综合一区二区 | 十八禁人妻一区二区| 两个人看的免费小视频| 国产精品综合久久久久久久免费| 黄色视频,在线免费观看| 婷婷丁香在线五月| 老司机在亚洲福利影院| 亚洲,欧美精品.| 国产美女午夜福利| 成人一区二区视频在线观看| ponron亚洲| 久久久久久久久久黄片| 最近在线观看免费完整版| 人人妻人人看人人澡| 91av网一区二区| 久久久久久久精品吃奶| 欧美国产日韩亚洲一区| 国产亚洲精品久久久久久毛片| 99热只有精品国产| 免费观看的影片在线观看| 欧美一级a爱片免费观看看| 精品电影一区二区在线| 嫩草影院精品99| 亚洲欧美精品综合久久99| 成年版毛片免费区| 久久精品国产清高在天天线| 禁无遮挡网站| 成人欧美大片| 国产三级在线视频| 久久久久久久精品吃奶| 午夜久久久久精精品| 亚洲一区二区三区色噜噜| 久久香蕉国产精品| www国产在线视频色| 老司机在亚洲福利影院| 99国产综合亚洲精品| 一级毛片女人18水好多| 日本 av在线| 国产97色在线日韩免费| 两个人的视频大全免费| 国产精品 国内视频| 国产伦在线观看视频一区| 天堂√8在线中文| 99久久成人亚洲精品观看| 国产成人系列免费观看| 狂野欧美白嫩少妇大欣赏| 久久精品91蜜桃| 亚洲国产精品成人综合色| 中文字幕av在线有码专区| 国产精品,欧美在线| 国产成人欧美在线观看| 亚洲专区中文字幕在线| 国产精品精品国产色婷婷| 最近在线观看免费完整版| 成人国产综合亚洲| 天堂√8在线中文| 一级作爱视频免费观看| 亚洲无线观看免费| eeuss影院久久| 亚洲国产精品成人综合色| 在线播放国产精品三级| 亚洲人成网站在线播放欧美日韩| 国产精品,欧美在线| 在线观看免费午夜福利视频| 熟妇人妻久久中文字幕3abv| 久久久成人免费电影| 真人一进一出gif抽搐免费| 亚洲午夜理论影院| 中文字幕人成人乱码亚洲影| www.www免费av| 国产精品野战在线观看| 91av网一区二区| 男插女下体视频免费在线播放| 午夜免费男女啪啪视频观看 | 国产精品精品国产色婷婷| 脱女人内裤的视频| 亚洲,欧美精品.| 日本在线视频免费播放| 日韩 欧美 亚洲 中文字幕| av片东京热男人的天堂| 三级男女做爰猛烈吃奶摸视频| 亚洲精品粉嫩美女一区| 桃色一区二区三区在线观看| 老熟妇乱子伦视频在线观看| 久久精品国产清高在天天线| 99精品久久久久人妻精品| 亚洲va日本ⅴa欧美va伊人久久| 高清日韩中文字幕在线| 十八禁人妻一区二区| 高潮久久久久久久久久久不卡| 婷婷精品国产亚洲av在线| 高清毛片免费观看视频网站| 免费av不卡在线播放| 亚洲av熟女| 久久精品国产亚洲av涩爱 | 热99re8久久精品国产| 成人av一区二区三区在线看| 嫁个100分男人电影在线观看| 国产精品1区2区在线观看.| 19禁男女啪啪无遮挡网站| 蜜桃亚洲精品一区二区三区| 亚洲专区国产一区二区| 长腿黑丝高跟| 美女高潮的动态| 在线观看免费视频日本深夜| 黄色视频,在线免费观看| www.熟女人妻精品国产| 12—13女人毛片做爰片一| 91久久精品国产一区二区成人 | 在线播放无遮挡| 男人舔女人下体高潮全视频| 日韩欧美精品免费久久 | 99热这里只有精品一区| 国内精品一区二区在线观看| 99热只有精品国产| 国产视频内射| 婷婷精品国产亚洲av| 久久精品综合一区二区三区| 成人亚洲精品av一区二区| 亚洲成人久久性| 亚洲av熟女| 国产精品电影一区二区三区| 国产一区二区在线观看日韩 | 午夜免费男女啪啪视频观看 | 国模一区二区三区四区视频| 国产精品99久久久久久久久| 国产精品三级大全| 午夜久久久久精精品| 日韩欧美精品v在线| 美女cb高潮喷水在线观看| 啦啦啦观看免费观看视频高清| 欧美又色又爽又黄视频| 国产亚洲av嫩草精品影院| 亚洲国产欧美网| 国产精品一及| 国产日本99.免费观看| 全区人妻精品视频| 狠狠狠狠99中文字幕| 在线国产一区二区在线| 美女大奶头视频| 日本一二三区视频观看| 欧美精品啪啪一区二区三区| 九色成人免费人妻av| 91麻豆av在线| 国产乱人视频| 美女cb高潮喷水在线观看| 香蕉久久夜色| 日韩 欧美 亚洲 中文字幕| 在线免费观看不下载黄p国产 | 亚洲aⅴ乱码一区二区在线播放| a级一级毛片免费在线观看| 黄片小视频在线播放| 法律面前人人平等表现在哪些方面| 成人av一区二区三区在线看| 久久6这里有精品| 国产在视频线在精品| 国产一级毛片七仙女欲春2| 亚洲av免费在线观看| 午夜激情福利司机影院| 好看av亚洲va欧美ⅴa在| 日韩欧美三级三区| 成人鲁丝片一二三区免费| 国产一级毛片七仙女欲春2| 日本a在线网址| 色综合婷婷激情| 日本精品一区二区三区蜜桃| 99热这里只有精品一区| 草草在线视频免费看| 精品久久久久久成人av| av中文乱码字幕在线| 久久九九热精品免费| 两性午夜刺激爽爽歪歪视频在线观看| 国产精品免费一区二区三区在线| 亚洲成a人片在线一区二区| 小说图片视频综合网站| 国内毛片毛片毛片毛片毛片| a级一级毛片免费在线观看| 一个人免费在线观看的高清视频| 亚洲一区二区三区不卡视频| 亚洲七黄色美女视频| 老司机在亚洲福利影院| 日韩欧美精品免费久久 | 国内揄拍国产精品人妻在线| 精品久久久久久久久久久久久| 免费电影在线观看免费观看| 国产成人av激情在线播放| 老汉色av国产亚洲站长工具| 亚洲中文日韩欧美视频| 亚洲性夜色夜夜综合| 国产精品乱码一区二三区的特点| 欧美黑人巨大hd| 精品不卡国产一区二区三区| 亚洲五月天丁香| 又黄又爽又免费观看的视频| 黄色视频,在线免费观看| 午夜久久久久精精品| 国产不卡一卡二| 亚洲成a人片在线一区二区| 婷婷精品国产亚洲av| 90打野战视频偷拍视频| 免费看日本二区| 中文亚洲av片在线观看爽| 欧美在线一区亚洲| 国产亚洲精品综合一区在线观看| 色综合亚洲欧美另类图片| 国产亚洲欧美98| 悠悠久久av| 欧美成人一区二区免费高清观看| 免费高清视频大片| 五月玫瑰六月丁香| 看片在线看免费视频| 叶爱在线成人免费视频播放| 少妇高潮的动态图| 午夜福利视频1000在线观看| 此物有八面人人有两片| 久久精品国产99精品国产亚洲性色| 国产主播在线观看一区二区| 高潮久久久久久久久久久不卡| 亚洲无线在线观看| 又黄又爽又免费观看的视频| 午夜福利视频1000在线观看| 久久欧美精品欧美久久欧美| 成人性生交大片免费视频hd| 精品一区二区三区人妻视频| 老鸭窝网址在线观看| 熟妇人妻久久中文字幕3abv| 欧美日韩中文字幕国产精品一区二区三区| 国内久久婷婷六月综合欲色啪| 国产一区二区三区视频了| 一区二区三区免费毛片| 欧美+日韩+精品| 97超视频在线观看视频| 国产日本99.免费观看| 黄色成人免费大全| 一夜夜www| 精品久久久久久久人妻蜜臀av| 日韩欧美一区二区三区在线观看| 非洲黑人性xxxx精品又粗又长| 1000部很黄的大片| 嫩草影院入口| 搞女人的毛片| 琪琪午夜伦伦电影理论片6080| 九色国产91popny在线| 中文字幕人成人乱码亚洲影| 日本撒尿小便嘘嘘汇集6| 久久精品91蜜桃| 一个人观看的视频www高清免费观看| 国产亚洲精品综合一区在线观看| 97超视频在线观看视频| 在线播放国产精品三级| 婷婷六月久久综合丁香| 成熟少妇高潮喷水视频| 丁香欧美五月| 欧美成人免费av一区二区三区| 色在线成人网| 成人鲁丝片一二三区免费| 婷婷精品国产亚洲av在线| 热99re8久久精品国产| 身体一侧抽搐| 久久久久国内视频| 日韩国内少妇激情av| 久久久国产精品麻豆| 国产高清三级在线| 日韩 欧美 亚洲 中文字幕| 在线观看日韩欧美| 国语自产精品视频在线第100页| 国产精品自产拍在线观看55亚洲| 久久国产精品人妻蜜桃| 午夜福利18| 欧美色视频一区免费| 波野结衣二区三区在线 | 观看美女的网站| 脱女人内裤的视频| 亚洲片人在线观看| 在线看三级毛片| 亚洲中文字幕一区二区三区有码在线看| 18禁美女被吸乳视频| 中文字幕高清在线视频| 18禁国产床啪视频网站| 婷婷丁香在线五月| 免费看a级黄色片| 日韩亚洲欧美综合| 婷婷精品国产亚洲av| 超碰av人人做人人爽久久 | 老熟妇仑乱视频hdxx| 亚洲五月婷婷丁香| av天堂中文字幕网| 久久精品91无色码中文字幕| 欧美在线一区亚洲| 欧美一级a爱片免费观看看| 久久亚洲精品不卡| 欧美3d第一页| 中文字幕av成人在线电影| 毛片女人毛片| 亚洲成人免费电影在线观看| 91在线精品国自产拍蜜月 | 国产成年人精品一区二区| 亚洲国产精品久久男人天堂| 亚洲精品日韩av片在线观看 | 亚洲国产日韩欧美精品在线观看 | 99久久综合精品五月天人人| 2021天堂中文幕一二区在线观| 可以在线观看的亚洲视频| 日韩中文字幕欧美一区二区| 久久精品91无色码中文字幕| 免费av不卡在线播放| 亚洲精华国产精华精| 欧美乱色亚洲激情| 免费大片18禁| 精品久久久久久久人妻蜜臀av| 十八禁网站免费在线| 久久国产精品人妻蜜桃| 婷婷六月久久综合丁香| 国产v大片淫在线免费观看| 此物有八面人人有两片| 麻豆国产av国片精品| 在线天堂最新版资源| 欧美成人一区二区免费高清观看| 内射极品少妇av片p| 叶爱在线成人免费视频播放| a级一级毛片免费在线观看| 日本精品一区二区三区蜜桃| 真实男女啪啪啪动态图| 美女大奶头视频| 日日干狠狠操夜夜爽| 韩国av一区二区三区四区| 国产亚洲欧美在线一区二区| 中文字幕久久专区| 丰满人妻一区二区三区视频av | 国产亚洲精品综合一区在线观看| 国产精品一区二区三区四区久久| 级片在线观看| 国产精品久久久人人做人人爽| 欧美一级毛片孕妇| 91av网一区二区| 日本黄色片子视频| 日本五十路高清| 非洲黑人性xxxx精品又粗又长| 欧美日本亚洲视频在线播放| 又爽又黄无遮挡网站| 搡老岳熟女国产| 日本在线视频免费播放| 欧美最黄视频在线播放免费| 亚洲乱码一区二区免费版| 国产成人aa在线观看| 国产精品综合久久久久久久免费| 欧美成狂野欧美在线观看| 国产精品久久久久久久电影 | 香蕉久久夜色| 国产精品女同一区二区软件 | 色综合站精品国产| 啦啦啦韩国在线观看视频| 亚洲av成人av| 怎么达到女性高潮| 国产欧美日韩精品亚洲av| 亚洲av成人精品一区久久| 亚洲av美国av| 最近最新中文字幕大全电影3| 男女做爰动态图高潮gif福利片| 国产色爽女视频免费观看| 欧美日韩中文字幕国产精品一区二区三区| 国产熟女xx| 亚洲aⅴ乱码一区二区在线播放| 成人特级黄色片久久久久久久| 精品熟女少妇八av免费久了| 中文字幕av成人在线电影| 在线免费观看不下载黄p国产 | 非洲黑人性xxxx精品又粗又长| 国产av在哪里看| 亚洲精品乱码久久久v下载方式 | 高清在线国产一区| 波多野结衣高清作品| 波多野结衣巨乳人妻| 日韩欧美免费精品| 国语自产精品视频在线第100页| 欧美xxxx黑人xx丫x性爽| av天堂中文字幕网| tocl精华| 亚洲电影在线观看av| 成人av在线播放网站| 欧美又色又爽又黄视频| 免费看日本二区|