Our research is focused on examining the video quality assessment model based on the MPEG-7 descriptor. This
model consists of two parts: "Frame Quality estimation processing" and "Video Quality estimation processing".
The estimation of Video Quality in the proposed model uses five values (average value, worst value, best value,
standard deviation and frame rate) from the estimation Frame Quality and the input video sequence. Two
coding methods (WMV9 and H.264) are used to verify the proposed model's presumption accuracy. As a result,
Video Quality estimation has a high presumption accuracy (correlation : 0.94, average error : 0.20, maximum
error : 0.68 and outlier ratio : 0.23).
Display of stereo images is widely used to enhance the viewing experience of three-dimensional imaging and
communication systems. In this paper, we propose a method for estimating the quality of stereoscopic images
using segmented image features and disparity. This method is inspired by the human visual system. We believe
the perceived distortion and disparity of any stereoscopic display is strongly dependent on local features, such
as edge (non-plane) and non-edge (plane) areas. Therefore, a no-reference perceptual quality assessment is
developed for JPEG coded stereoscopic images based on segmented local features of artifacts and disparity.
Local feature information such as edge and non-edge area based relative disparity estimation, as well as the
blockiness and the blur within the block of images are evaluated in this method. Two subjective stereo image
databases are used to evaluate the performance of our method. The subjective experiments results indicate our
model has sufficient prediction performance.
This research aims to develop an objective no-reference video quality evaluation method for MPEG-2 MP@ML
coded (symmetric and asymmetric) stereoscopic videos. Our proposed method is based on segmented local
features of spatial artifacts, disparity, and temporal activities of videos. Segmented local features information
such as edge and non-edge areas of any stereoscopic pair frames (i.e., left and right views) have taken into
consideration for blockiness and zero crossing. In this method, a temporal segmentation approach is considered
and each temporal segment is evaluated for artifacts and disparity. Temporal features are calculated separately for
left and right video sequences based on segmented local features and sub temporal segment. Different weighting
factors are also applied to measure the spatial artifacts, disparity, and temporal features of the segment. In
order to verify the performance, we conducted subjective experiment on different symmetric and asymmetric
coded (Bit rates: 2, 3, 5, and 8 Mbps) stereo video pairs. An auto stereoscopic display was used for fifteen
(15) reference stereo videos; each of the video was 15 seconds length and the total length of each test sequence
was (15×15 sec = 3 min 45 sec). Seven video sequences were used to complete the experiment. The Single
Stimulus Continuous Quality Evaluation (SSCQE) method was used to conduct our subjective experiment. The
experiment result indicates that our proposed method has given sufficient prediction performance.
The importance of the perceived quality measurement is fundamental for many image processing applications, such as compression, acquisition, restoration, enhancement, and reproduction. Color information is also of great importance for the perceived image quality, although perceived information is mainly represented by luminance. We present a computational and memory-efficient no-reference image quality assessment model independent of JPEG and JPEG2000 coded color images based on local regions. We also present the discrimination algorithm for these two types of coded images. The features of local regions are blockiness around the block boundary, average absolute difference between adjacent pixels within the block, and zero crossing rate within the block of the image. We validate the performance of our model on our subjective database, which shows good quality prediction performance, and the model's generalization ability is also verified on the other database.
To perform Quality of Service (QoS) control of video communication more efficiently, it is necessary to develop an objective quality evaluation method for coded video. Many proposed conventional methods to obtain video quality require the availability of both reference and processed video sequence. However, in case of re-encoding
the coded video stream at the receiver side where reference video sequence is not present, it is impossible to do such a full-reference evaluation. Therefore, we have developed a video quality evaluation model by using reduced reference for evaluated value obtained by SSCQE method. In this approach, we use some features extracted from
reference video. It is called reduced-reference method by VQEG. Transmitting these features with coded video, the proposed model can estimate the video quality, even in the absent of the full original video in the decoder side. The video quality rating obtained from the proposed model shows good agreement with subjective quality.
Video transmission on the Internet often requires a transcoding in order for the bitrate to meet the available bandwidth. Since the bitrate of videos is determined by an amount of bits per frame(BPF) and framerate(FPS), one of or either of these should be reduced in transcoding. In addition, because there is a tradeoff between the BPF and FPS under a bitrate constraint, the BPF and FPS should be optimally determined to get the best subjective video quality. This paper proposes a framerate optimization method, which estimates an optimal framerate directly from a target video and a specified bitrate. Actually, an optimal framerate is derived by using a characteristic surface of Mean Opinion Score(MOS), which is estimated by suitable features of a target video. Comparison of the optimal framerate obtained by the actual measurement with one by the proposed technique has proved that the accuracy of estimation by the proposed technique is satisfactory for several open data sequences.
To consider the quality of service for stereoscopic image through the network, it is necessary to develop a quality evaluation method for coded stereoscopic image. We propose a quality evaluation model of the coded stereoscopic color image. This evaluation model considers not only the distortions of the edge region and smooth region but also the texture features of the left image. In addition, this model takes into account the disparity information between the left and right images. Instead of the disparity compensated coded image, we employ the JPEG coded image for the subjective assessment test. As the results, the evaluation model is useful for coded stereoscopic image.
The main media in multimedia communication are moving images, and the development of the objective quality evaluation method of the color moving image is strongly hoped for. In this research, as the preparatory steps for the development of the objective quality evaluation method, the subjective evaluation experiment was done using the semantic differential method, to clarify factors of the subjective quality evaluation in which the moving image was encoded using the MC plus DCT. These factors are the basic components which evaluate objectively the picture quality. Moreover, the difference of the evaluation factors was analyzed, it compared with the result of the subjective evaluation experiment of the intra-frame coding and the inter-frame coding. Next, the subjective evaluation experiment was done by the EBU method and the relation between the subjective evaluation factors which is derived from the SD method and the scale of the quality degradation (MOS) was investigated.
In studying high efficiency color image coding and image processing, it is important to segment several regions that represent real objects. By performing this region segmentation, we can establish the structural description using the characteristic information of regions. To segment the image into several characteristic regions, we adopt the clustering algorithm in the Uniform Lightness Chromaticness Scale System and the merging process based on the measure of Godlove's color difference.