Video Quality Rate Research Articles

Evaluation of TikTok videos on acute pancreatitis: content quality and reliability analysis

BackgroundAcute pancreatitis (AP) is a common acute digestive system disorder, with patients often turning to TikTok for AP-related information. However, the platform’s video quality on AP has not been thoroughly investigated.ObjectiveThe main purpose of this study is to evaluate the quality of videos about AP on TikTok, and the secondary purpose is to study the related factors of video quality.MethodsThis study involved retrieving AP-related videos from TikTok, determining, and analyzing them based on predefined inclusion and exclusion criteria. Relevant data were extracted and compiled for evaluation. Video quality was scored using the DISCERN instrument and the Health on the Net (HONcode) score, complemented by introducing the Acute Pancreatitis Content Score (APCS). Pearson correlation analysis was used to assess the correlation between video quality scores and user engagement metrics such as likes, comments, favorites, retweets, and video duration.ResultsA total of 111 TikTok videos were included for analysis, and video publishers were composed of physicians (89.18%), news media organizations (13.51%), individual users (5.41%), and medical institutions (0.9%). The majority of videos focused on AP-related educational content (64.87%), followed by physicians’ diagnostic and treatment records (15.32%), and personal experiences (19.81%). The mean scores for DISCERN, HONcode, and APCS were 33.05 ± 7.87, 3.09 ± 0.93, and 1.86 ± 1.30, respectively. The highest video scores were those posted by physicians (35.17 ± 7.02 for DISCERN, 3.31 ± 0.56 for HONcode, and 1.94 ± 1.34 for APCS, respectively). According to the APCS, the main contents focused on etiology (n = 55, 49.5%) and clinical presentations (n = 36, 32.4%), followed by treatment (n = 24, 21.6%), severity (n = 20, 18.0%), prevention (n = 19, 17.1%), pathophysiology (n = 17, 15.3%), definitions (n = 13, 11.7%), examinations (n = 10, 9%), and other related content. There was no correlation between the scores of the three evaluation tools and the number of followers, likes, comments, favorites, and retweets of the video. However, DISCERN (r = 0.309) and APCS (r = 0.407) showed a significant positive correlation with video duration, while HONcode showed no correlation with the duration of the video.ConclusionsThe general quality of TikTok videos related to AP is poor; however, the content posted by medical professionals shows relatively higher quality, predominantly focusing on clinical presentations and etiologies. There is a discernible correlation between video duration and quality ratings, indicating that a combined approach incorporating the guideline can comprehensively evaluate AP-related content on TikTok.

QoE-Based Performance Comparison of AVC, HEVC, and VP9 on Mobile Devices with Additional Influencing Factors

While current video quality assessment research predominantly revolves around resolutions of 4 K and beyond, targeted at ultra high-definition (UHD) displays, effective video quality for mobile video streaming remains primarily within the range of 480 p to 1080 p. In this study, we conducted a comparative analysis of the quality of experience (QoE) for widely implemented video codecs on mobile devices, specifically Advanced Video Coding (AVC), its successor High-Efficiency Video Coding (HEVC), and Google’s VP9. Our choice of 720 p video sequences from a newly developed database, all with identical bitrates, aimed to maintain a manageable subjective assessment duration, capped at 35–40 min. To mimic real-time network conditions, we generated stimuli by streaming original video clips over a controlled emulated setup, subjecting them to eight different packet-loss scenarios. We evaluated the quality and structural similarity of the distorted video clips using objective metrics, including the Video Quality Metric (VQM), Peak Signal-to-Noise Ratio (PSNR), Video Multi-Method Assessment Fusion (VMAF), and Multi-Scale Structural Similarity Index (MS-SSIM). Subsequently, we collected subjective ratings through a custom mobile application developed for Android devices. Our findings revealed that VMAF accurately represented the degradation in video quality compared to other metrics. Moreover, in most cases, HEVC exhibited an advantage over both AVC and VP9 under low packet-loss scenarios. However, it is noteworthy that in our test cases, AVC outperformed HEVC and VP9 in scenarios with high packet loss, based on both subjective and objective assessments. Our observations further indicate that user preferences for the presented content contributed to video quality ratings, emphasizing the importance of additional factors that influence the perceived video quality of end users.

A quality enhancement network with coding priors for constant bit rate video coding

Video coding can effectively compress data, while the introduction of compression artifacts degrades the visual quality and the performance of artificial intelligence video applications. Video quality enhancement (VQE) methods can improve compressed video quality without modifying the coding standard’s main modules. The existing VQE works mainly aim to improve video quality in the constant quantization parameter (CQP) coding mode. However, constant bit rate (CBR) coding mode is widely used in some streaming playback video applications, and VQE at CBR is more challenging than that at CQP. This article presents a novel Constant bit rate Video quality enhancement Network combined with Coding priors (CVCN). The proposed CVCN can be inserted into High Efficiency Video Coding (HEVC) codec as a CNN-based in-loop filter (LF) module or a post-processing module out of the codec. Moreover, we design a two-pass training strategy for the LF module to overcome multiple filtering. To adapt the QP diversity of CBR videos, we utilize the coding unit (CU)-wise QP prior by constructing a CU-wise QP adaptive module (CQAM) and a QP adaptive multi-scale residual block (QAMSRB) based on CQAM. We construct the CU-partition prior (CPP) by exploiting the relationship between the compression noise and CU partition information. A novel CPP spatial-attention block is proposed to combine the CPP with the self-attention module. Furthermore, the proposed CVCN can effectively enhance CBR videos at different bits per pixel (BPP) via a single model. Extensive experimental results show the superiority of the CVCN over state-of-the-art VQE approaches for CBR videos.

FluentSigners-50: A signer independent benchmark dataset for sign language processing.

This paper presents a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) for the purposes of Sign Language Processing. We envision it to serve as a new benchmark dataset for performance evaluations of Continuous Sign Language Recognition (CSLR) and Translation (CSLT) tasks. The proposed FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers resulting in 43,250 video samples. Dataset contributors recorded videos in real-life settings on a wide variety of backgrounds using various devices such as smartphones and web cameras. Therefore, distance to the camera, camera angles and aspect ratio, video quality, and frame rates varied for each dataset contributor. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life sign language. FluentSigners-50 baseline is established using two state-of-the-art methods, Stochastic CSLR and TSPNet. To this end, we carefully prepared three benchmark train-test splits for models' evaluations in terms of: signer independence, age independence, and unseen sentences. FluentSigners-50 is publicly available at https://krslproject.github.io/FluentSigners-50/.

Acceptability and Feasibility of Community Management of Ear Patients through Tele-otology Services in Outskirts of Kathmandu Valley, Nepal

INTRODUCTION: Tele-otology is one of the least explored areas of telehealth in Nepal. The objective image findings and live virtual specialist consultation using synchronous data exchange can address the different barriers to basic otological care in low-resource settings like Nepal. This study was conducted to explore the acceptability and feasibility of community management of ear patients through tele-otology. MATERIALS AND METHODS: It was a cross-sectional descriptive study conducted at a tertiary level ENT hospital in Bhaktapur and three outreach clinics of the hospital in the periphery of Kathmandu valley from 1st April to 31st May 2021. Trained community ear health workers (CEHWs) used mobile data in fields and communicated with ENT consultants at the base hospital with fiber-to-the-home internet connection for teleconsultation. The satisfaction was measured using a 5-point Likert scale. RESULTS: This study has shown satisfactory acceptance among beneficiaries and service providers. The sound quality rating was 4.5±0.8 at field-site and 4.5±0.9 at base hospital. The video quality ratings were 4.4±0.8 and 4.0±1.1 at field-site and base hospital, respectively; this difference was statistically significant (p=0.017). CONCLUSIONS: With trained grassroots level workers and quality internet connections, tele-otology can play a significant role in the early identification and diagnosis of ear diseases, including middle ear pathologies, as well as bridging the ear care service gap in resource constraints settings like Nepal.

CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement

Constant bit rate (CBR) videos are widely used in streaming playback applications. However, the image quality of the CBR video is often unstable, especially for scenes with large motion. To this end, we design a new model to represent the distortion of High Efficiency Video Coding (HEVC) constant bit rate video, and propose a neural network for a constant bit rate video quality enhancement (CBREN). We propose a dual-domain restoration module (DRM) to jointly learn the prior knowledge in the pixel domain and the frequency domain. To address the degradation resulting from compression, we propose a two-step quantization degradation estimation strategy. The Inverse DCT (IDCT) Translation Unit (ITU) is used to constrain the quantization table of the constant bit rate video to a suitable range, and the Dynamic Alpha Unit (DAU) is used to fine-tune the quantization table according to the content of each frame. In order to effectively reduce the block distortion of different sizes produced in the compression process, we adopt a multi-scale network. Extensive experiments show that our approach can greatly enhance the quality of CBR compressed video. Moreover, our method can also be applied to constant quantization parameter (CQP) video enhancement tasks, and is certainly superior to existing methods.

AVQBits—Adaptive Video Quality Model Based on Bitstream Information for Various Video Applications

The paper presents <i>AV QBits</i>, a versatile, bitstream-based video quality model. It can be applied in several contexts such as video service monitoring, evaluation of video encoding quality, of gaming video QoE, and even of omnidirectional video quality. In the paper, it is shown that <i>AV QBits</i> predictions closely match video quality ratings obained in various subjective tests with human viewers, for videos up to 4K-UHD resolution (Ultra-High Definition, 3840 x 2180 pixels) and framerates up 120 fps. With the different variants of <i>AV QBits</i> presented in the paper, video quality can be monitored either at the client side, in the network or directly after encoding. The no-reference <i>AV QBits</i> model was developed for different video services and types of input data, reflecting the increasing popularity of Video-on-Demand services and widespread use of HTTP-based adaptive streaming. At its core, <i>AV QBits</i> encompasses the standardized ITU-T P.1204.3 model, with further model instances that can either have restricted or extended input information, depending on the application context. Four different instances of <i>AV QBits</i> are presented, that is, a Mode 3 model with full access to the bitstream, a Mode 0 variant using only metadata such as codec type, framerate, resoution and bitrate as input, a Mode 1 model using Mode 0 information and frame-type and -size information, and a Hybrid Mode 0 model that is based on Mode 0 metadata and the decoded video pixel information. The models are trained on the authors&#x2019; own AVT-PNATS-UHD-1 dataset described in the paper. All models show a highly competitive performance by using AVT-VQDB-UHD-1 as validation dataset, e.g., with the Mode 0 variant yielding a value of 0.890 Pearson Correlation, the Mode 1 model of 0.901, the hybrid no-reference mode 0 model of 0.928 and the model with full bitstream access of 0.942. In addition, all four <i>AV QBits</i> variants are evaluated when applying them out-of-the-box to different media formats such as 360&#x00B0; video, high framerate (HFR) content, or gaming videos. The analysis shows that the ITU-T P.1204.3 and Hybrid Mode 0 instances of <i>AV QBits</i> for the considered use-cases either perform on par with or better than even state-of-the-art full reference, pixel-based models. Furthermore, it is shown that the proposed Mode 0 and Mode 1 variants outperform commonly used no-reference models for the different application scopes. Also, a long-term integration model based on the standardized ITU-T P.1203.3 is presented to estimate ratings of overall audiovisual streaming Quality of Experience (QoE) for sessions of 30 s up to 5 min duration. In the paper, the <i>AV QBits</i> instances with their per-1-sec score output are evaluated as the video quality component of the proposed long-term integration model. All <i>AV QBits</i> variants as well as the long-term integration module are made publicly available for the community for further research.

RETRACTED ARTICLE: Big data VR system oriented to cloud services in Spanish cultural video simulation application

The data volume of cloud service big data is huge, usually more than 10 TB, and it has even jumped to the petabyte level. Big data VR has a variety of data, not only structured, but also semi-structured, unstructured and interactive. In the Internet multimedia era, the output of images, sounds, videos and other data is amazing, so semi-structured and unstructured data are very important to a large extent. However, the main data of cloud services is not only a huge amount of data, but also has its diversity. The speed of big data VR is not only the speed of big data generation, but also the speed of processing. According to data, the amount of global data is growing at a rate of 50% per year, far exceeding the development speed of computer design. Therefore, processing and analyzing large data should be faster. Another way of spreading Spanish culture is non-governmental cultural exchanges. For example, some Chinese and Western art festivals held between China and Spain. China will also have some non-governmental cultural organizations about Spain, which can spread Spanish culture. Video simulation is a new type of video carrier, which has the characteristics of sound and image alignment, giving users a sense of in-depth immersion. Compared with traditional video images, virtual reality requires a higher bit rate, and also takes up more network bandwidth and storage space. While giving users a high-quality experience, how to optimize video quality and low bit rate video encoding is video simulation, one of the keys to rapid development, which is also the focus of this article.

Dynamic video delivery using deep reinforcement learning for device-to-device underlaid cache-enabled Internet-of-vehicle networks

This paper addresses an Internet-of-vehicle network that utilizes a device-to-device (D2D) underlaid cellular system, where distributed caching at each vehicle is available and the video streaming service is provided via D2D links. Given the spectrum reuse policy, three decisions having different timescales in such a D2D underlaid cache-enabled vehicular network were investigated: 1) The decision on the cache-enabled vehicles for providing contents, 2) power allocation for D2D users, and 3) power allocation for cellular vehicles. Since wireless link activation for video delivery could introduce delays, node association is determined in a larger time scale compared to power allocations. We jointly optimize these delivery decisions by maximizing the average video quality under the constraints on the playback delays of streaming users and the data rate guarantees for cellular vehicles. Depending on the channel and queue states of users, the decision on the cache-enabled vehicle for video delivery is adaptively made based on the frame-based Lyapunov optimization theory by comparing the expected costs of vehicles. For each cache-enabled vehicle, the expected cost is obtained from the stochastic shortest path problem that is solved by deep reinforcement learning without the knowledge of global channel state information. Specifically, the deep deterministic policy gradient (DDPG) algorithm is adopted for dealing with the very large state space, i.e., time-varying channel states. Simulation results verify that the proposed video delivery algorithm achieves all the given goals, i.e., average video quality, smooth playback, and reliable data rates for cellular vehicles.

IJNM Special Issue—International Journal of Network Management: QoE‐centric analysis and management of communication networks

IJNM Special Issue—International Journal of Network Management: QoE‐centric analysis and management of communication networks

Decoding-Complexity-Aware HEVC Encoding Using a Complexity–Rate–Distortion Model

The energy consumption of consumer electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device’s the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper first performs an analysis to determine the tradeoff between the decoding complexity, video quality, and bit rate with respect to a reference decoder implementation on a general purpose processor architecture. Thereafter, a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity–rate–distortion optimized High Efficiency Video Coding (HEVC) bit streams. The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams.

An Adaptive Quad-Tree Depth Range Prediction Mechanism for HEVC

A next-generation video coding standard High Efficiency Video Coding (HEVC) provides higher video quality and lower compression bit rate but leads to very high encoding complexity, especially in the quad-tree-based coding tree unit partitioning process. To reduce the computational complexity of HEVC, in this paper, we propose an adaptive quad-tree depth range prediction mechanism. First, the proposed mechanism defines the similar region flag to distinguish between the similar region and the non-similar region. Then, two algorithms, the similar region depth range prediction algorithm and the non-similar region depth range prediction algorithm, are proposed. The similar region depth range prediction algorithm estimates the features of the similar region based on the coding unit depth of this region. The optimal depth of this region can be predicted. The non-similar region depth range prediction algorithm can skip low probability tree nodes based on the depth correlation coefficient, which is calculated based on scene content change. Both the similar region depth range prediction algorithm and the non-similar region depth range prediction algorithm show more than 90% predictive accuracy. Experimental results show that under random access configuration and low delay configuration, the proposed mechanism can yield 28.17% and 32.99% computational complexity reduction with negligible rate distortion performance loss, respectively, compared with HM16.9. The results show that the proposed mechanism is expected to be applied in real-time environments.

Techniques for dynamic hardware management of streaming media applications using a framework for system scenarios

Many modern applications exhibit dynamic behavior, which can be exploited for reduced energy consumption. We employ a two-phase combined design-time/run-time methodology that identifies different run-time situations and clusters similar behaviors into system scenarios. This methodology is integrated with our framework for system scenario based designs, which dynamically tune the hardware to match the application behavior. We focus on streaming media applications, and achieve significant energy reductions for an extracted control structure of a video codec widely used in hand-held devices today. We encode a video stream consisting of different frame sizes based on measured available wireless bandwidth. Energy consumption is measured with a modified microcontroller board from Atmel with two alternative voltage and frequency settings. While maintaining the perceptual video quality and frame rate, our method results in up to 49% energy reduction for our encoded streams. The average tuning overhead of our mechanism is negligible after applying simple loop transformations to the encoder control structure. We furthermore show how to obtain up to 34.3% energy reductions for a system with limited slack available, by dynamically exploiting the available sleep modes.

Comparative Analysis of Three Different Modalities for Perception of Artifacts in Videos

This study compares three popular modalities for analyzing perceived video quality; user ratings, eye tracking, and EEG. We contrast these three modalities for a given video sequence to determine if there is a gap between what humans consciously see and what we implicitly perceive . Participants are shown a video sequence with different artifacts appearing at specific distances in their field of vision; near foveal, middle peripheral, and far peripheral. Our results show distinct differences between what we saccade to (eye tracking), how we consciously rate video quality, and our neural responses (EEG data). Our findings indicate that the measurement of perceived quality depends on the specific modality used.

Mobile Endoscopy vs Video Tower

To determine if any significant difference exists between endoscopic videos captured with a mobile adaptor and videos captured with a traditional tower. Prospective controlled blinded comparison of mobile endoscopic videos captured through 2 methods. Thirty randomly selected patients underwent video endoscopy with both mobile and video tower recording methods. Sixty videos were edited into a series of 10-second clips. Thirteen otolaryngology staff and residents rated the video quality and provided a diagnosis for each video. We found no significant difference in the video quality ratings between mobile and tower videos (mean difference, -0.07; P < .37). Similarly, we found no significant difference in the observers' diagnostic accuracy (mean difference, 1.54%; P < .686). With adequate power, our study was unable to demonstrate a difference between mobile adapter videos and tower videos. Our findings suggest that mobile adapter videos may reasonably be used in lieu of tower videos in clinical practice.

Abstract TP250: Defining Technical Quality Standards for Mobile Telestroke Assessment: A Substudy of the Improving Treatment with Rapid Evaluation of Acute Stroke via Mobile Telemedicine (iTREAT) Mobile Telestroke Platform

Introduction: In-ambulance use of remote videoconferencing for prehospital stroke assessment (mobile telestroke) is an emerging innovation in acute stroke care. As a new technology, there is a dearth of technical standards to ensure transmission quality and guide deployment in various EMS settings. Hypothesis: Subjective video quality ratings during in-vehicle mobile telestroke assessment correlate to objective video data transmission metrics. Methods: We performed videoconferencing via a low-cost, utilitarian mobile telestroke platform: tablet endpoint, high-speed 4G LTE modem, external antennae, HIPAA-secure videoconferencing application, and portable bracket mounting. We held test calls along typical ambulance routes recording transmission quality by a stationary and a mobile rater. We used a standardized 6-point scale of video quality: rating ≥ 4 deemed acceptable for mobile telestroke assessment. We recorded jitter, the variance in transmission data reception order, as simultaneously reported by the videoconferencing application. Results: We completed five test runs yielding 64 data ratings. Average jitter for ratings 1 through 6 was 434.9ms (SD = 407), 106.1ms (SD = 110), 41.4ms (SD = 29), 35.3ms (SD = 15), 29.5ms (SD = 6), and 29.0ms (SD = 2) respectively. Analyzing the raw data yielded an R2 of 0.41. As seen in Chart 1, video quality decreased as average jitter increased, but jitter values as low as 30ms were still seen across video transmission of all qualities. Conclusion: These preliminary data suggest modest correlation of transmission variance with subjective quality ratings using a low-cost mobile telestroke platform along rural-based ambulance routes. However, average transmission variance correlated highly (R2 = 0.93) suggesting more data ratings may improve the correlation. Testing of our mobile telestroke platform to assess performance and clinical efficacy as well as incorporate live acute stroke encounters is ongoing.

Mobile Telestroke During Ambulance Transport Is Feasible in a Rural EMS Setting: The iTREAT Study.

The use of telemedicine in the diagnosis and treatment of acute stroke, or telestroke, is a well-accepted method of practice improving geographic disparities in timely access to neurological expertise. We propose that mobile telestroke assessment during ambulance transport is feasible using low-cost, widely available technology. We designed a platform including a tablet-based end point, high-speed modem with commercial wireless access, external antennae, and portable mounting apparatus. Mobile connectivity testing was performed along six primary ambulance routes in a rural network. Audiovisual (AV) quality was assessed simultaneously by both an in-vehicle and an in-hospital rater using a standardized 6-point rating scale (≥4 indicating feasibility). We sought to achieve 9 min of continuous AV connectivity presumed sufficient to perform mobile telestroke assessments. Thirty test runs were completed: 93% achieved a minimum of 9 min of continuous video transmission with a mean mobile connectivity time of 18 min. Mean video and audio quality ratings were 4.51 (4.54 vehicle; 4.48 hospital) and 5.00 (5.13 in-vehicle; 4.87 hospital), respectively. Total initial cost of the system was $1,650 per ambulance. In this small, single-centered study we maintained high-quality continuous video transmission along primary ambulance corridors using a low-cost mobile telemedicine platform. The system is designed to be portable and adaptable, with generalizability for rapid assessment of emergency conditions in which direct observational exam may improve prehospital diagnosis and treatment. Thus mobile telestroke assessment is feasible using low-cost components and commercial wireless connectivity. More research is needed to demonstrate clinical reliability and efficacy in a live-patient setting.

시간적 예측 구조와 움직임 벡터의 특성을 이용한 움직임 추정 기법

Efficient multi-view coding techniques are needed to reduce the complexity of multi-view video which increases in proportion to the number of cameras. To reduce the complexity and maintain image quality and bit-rates, an motion estimation method and temporal prediction structure are proposed in this paper. The proposed motion estimation method exploits the characteristic of motion vector distribution and the motion direction and motion size of the block to place search points and decide the search patten adaptively. And the proposed prediction structure divides every GOP to decide the maximum index of hierarchical B layer and the number of pictures of each B layer. Experiment results show that the complexity reduction of the proposed temporal prediction structure and motion estimation method over hierarchical B pictures prediction structure and TZ search method which are used in JMVC(Joint Multi-view Video Coding) reference model can be up to 45∼70% while maintaining similar video quality and bit rates.

VQone MATLAB toolbox: A graphical experiment builder for image and video quality evaluations: VQone MATLAB toolbox.

This article presents VQone, a graphical experiment builder, written as a MATLAB toolbox, developed for image and video quality ratings. VQone contains the main elements needed for the subjective image and video quality rating process. This includes building and conducting experiments and data analysis. All functions can be controlled through graphical user interfaces. The experiment builder includes many standardized image and video quality rating methods. Moreover, it enables the creation of new methods or modified versions from standard methods. VQone is distributed free of charge under the terms of the GNU general public license and allows code modifications to be made so that the program's functions can be adjusted according to a user's requirements. VQone is available for download from the project page (http://www.helsinki.fi/psychology/groups/visualcognition/).

다시점 비디오 부호화를 위한 개선된 예측 구조와 움직임 추정 기법

Multi-view video is obtained by capturing one three-dimensional scene with many cameras at different positions. The computational complexity of multi view video coding increases in proportion to the number of cameras. To reduce computational complexity and maintain the image quality, improved prediction structure and motion estimation method is proposed in this paper. The proposed prediction structure exploits an average distance between the current picture and its reference pictures. The proposed prediction structure divides every GOP into several groups to decide the maximum index of hierarchical B layer and the number of pictures of each B layer. And the proposed motion estimation method uses a hierarchical search strategy. This strategy method consists of modified diamond search pattern, progressive diamond search pattern and modified raster search pattern. Experiment results show that the complexity reduction of the proposed prediction structure and motion estimation method over JMVC (Joint Multiview Video Coding) reference model using hierarchical B pictures of Fraunhofer-HHI and TZ search method can be up to 40~70% while maintaining similar video quality and bit rates.