Jihwan Choe

Objective video quality assessment

We propose a new method for an objective measurement of video quality. By analyzing subjective scores of various video sequences, we find that the human visual system is particularly sensitive to degradation around edges. In other words, when edge areas of a video sequence are degraded, evaluators tend to give low quality scores to the video, even though the overall mean squared error is not large. Based on this observation, we propose an objective video quality measurement method based on degradation around edges. In the proposed method, we first apply an edge detection algorithm to videos and locate edge areas. Then, we measure degradation of those edge areas by computing mean squared errors and use it as a video quality metric after some postprocessing. Experiments show that the proposed method significantly outperforms the conventional peak signal-to-noise ratio (PSNR). This method was also independently evaluated by independent laboratory groups in the Video Quality Experts Group (VQEG) Phase 2 test. The method consistently provided good performances. As a result, the method was included in international recommendations for objective video quality measurement.

Estimation of the peak signal-to-noise ratio for compressed video based on generalized Gaussian modeling

The peak signal-to-noise ratio (PSNR) is one of the most popular video quality metrics. This ratio is computed using both original and processed images. We propose a new method to estimate the PSNR from an encoded bit stream without using original video sequences. In the proposed method, the transform coefficients of images or video frames are modeled by a generalized Gaussian distribution. By utilizing the model parameters of this distribution, the PSNR can be estimated. We also propose a fast method that can be used to estimate the model parameters of the original transform coefficient distribution using quantized transform coefficients as well as quantization information extracted from encoded bit streams. Experimental results with H.264 bit streams show that the proposed generalized Gaussian modeling method delivers better performance compared to the standard Laplacian modeling method when estimating the PSNR. The proposed method can be applied to image or video streams compressed with standard coding algorithms, such as MPEG-1, 2, 4, H.264, and JPEG. The proposed method can also be used for image or video quality monitoring systems on the receivers side.

Edge degradation for objective video quality metrics

In this paper, we propose a new method for an objective measurement of video quality based on edge degradation. One of the most important requirements for an objective method for video quality measurement is that it should provide consistent performances over a wide range of video sequences that are not used in the designing stage. By analyzing subjective scores of various video sequences, we found that the human visual system is sensitive to degradation around edges. In other words, when edge areas of a video are blurred, evaluators tend to give low scores to the video even though the overall mean squared error is not so large. Based on this observation, we propose an objective video quality measurement method that measures degradation around edges. In the proposed method, we first apply an edge detection algorithm to videos and find edge areas. Then, we measure degradation of those edge areas by computing mean squared error. From this mean squared error, we compute the PSNR and use it as video quality metric. Experimental results show that the proposed method compares favorably with the current objective methods for video quality measurement. Furthermore, when the proposed method is applied to test video sequences that are not used in the designing stage, it still consistently provides satisfactory performances.

Comparison of subjective video quality assessment methods for multimedia applications

In this paper, we compared two subjective assessment methods DSCQS(Double Stimulus Continuous Quality Scale method) and ACR(Absolute Category Rating). These methods are widely used in order to evaluate video quality for multimedia application. We performed subjective quality tests using DSCQS and ACR methods. The subjective scores obtained by the DSCQS and ACR methods show that these methods are highly correlated in terms of MOS(Mean Opinion Score) and have slightly lower correlation in terms of DMOS(Difference Mean Opinion Score). The results indicate that ACR method is an effective subjective quality assessment method, which shows compatible performance with DSCQS method and can evaluate a larger number of video sequences.

Dimension Reduction and Pre-emphasis for Compression of Hyperspectral Images

As the dimensionality of remotely sensed data increases, the need for efficient compression algorithms for hyperspectral images also increases. However, when hyperspectral images are compressed with conventional image compression algorithms, which have been developed to minimize mean squared errors, discriminant information necessary to distinguish among classes may be lost during compression process. In this paper, we propose to enhance such discriminant information prior to compression. In particular, we first find a new basis where class separability is better represented by applying a feature extraction method. However, due to high correlations between adjacent bands of hyperspectral data, we have singularity problems in applying feature extraction methods. In order to address the problem, we first reduce the dimension of data and then find a new basis by applying a feature extraction algorithm. Finally, dominant discriminant features are enhanced and the enhanced data are compressed using a conventional compression algorithm such as 3D SPIHT. Experiments show that the proposed compression method provides improved classification accuracies compared to the existing compression algorithms.

Fast implementation of 3-D SPIHT using tree information matrix

We apply a fast 3D-SPIHT to compress hyperspectral images. The method uses a tree information matrix to determine whether a tree is significant or insignificant during encoding process. Experiments with AVIRIS data show that it is possible to save about 70% of processing time.

Subjective video quality comparison on various display monitors

Recently, International Telecommunication Union approved several methods for objective video quality measurements to be included in international standards. These methods are mainly developed to measure quality of digital video on professional CRT monitors. In this paper, we performed subjective tests in order to examine the applicability of the standard quality assessment methods for different display monitors such as common CRT, PDP, and LCD TV monitors as well as professional CRT monitors. Subjective testing on different display monitors shows that there exist relatively high correlations between the various display monitors, which indicates the standard quality assessment methods can be used on the display monitors under the given conditions of subjective duality assessment.

Random access of compressed hyperspectral images

We propose a modified 3-D zerotree algorithm, which enables random access of compressed hyperspectral images by using an efficient memory structure and rearranging encoding order. This method reduces accessing time and requirements of network and system resources.

Compression of hyperspectral images at low bit rates

We propose a modified 3-D SPIHT algorithm with 3-D resizing, which is especially targeted for low bit rate compression. In the proposed algorithm, we first reduce 3-D images using a 3-D interpolation algorithm and encode the reduced 3-D images using a 3-D SPIHT algorithm. At the decoder, the encoded bit stream is first decoded and the reconstructed 3-D images are enlarged to the original image size. Experimental results show that the proposed algorithm outperforms the conventional 3-D SPIHT algorithm at low bit rates.

Fast and efficient video registration methods for full reference objective video quality assessment

In this paper, we propose a fast and efficient registration method that can be used for full reference objective video quality assessment. Instead of using full videos to register source video sequences and processed video sequences, we propose to use a number of reference frames which are selected under a certain criterion and select a number of sub-regions from the reference frames which have large variances. The wavelet transform is used to find the sub-regions. Since the registration is performed using the sub-regions, it can be fast. Experiments show promising results.