Kei Kawamura

Vector representation of binary images containing halftone dots

Vector representation of graphics has the advantage that the image can be displayed at any size. When the resolution of a bitmap image is changed, the lack of a line segment arises. In addition, moire occurs when the resolution of an image with halftone dots is changed. We propose a new technique to convert a binary image with halftone dots into its vector representation. Resolution conversion of a binary image can easily be performed without moire by using a continuous tone approximation of the halftone dots. First, we separate the area of halftone dots and line drawings in the image. Next, a continuous tone approximation is applied to the area of halftone dots. Then, conventional vectorization is applied to both continuous tone areas and line drawings. Finally, these components are mixed and reconstructed. Our approach provides an efficient way of displaying cartoon-like images at any size with a limited amount of data.

Gradation approximation for vector based compression of comic images

In this paper, we propose a method to vectorize comic images including halftone dots. Our method can prevent the jaggy and moire phenomena when the images are enlarged and shrank. The method contains three modules: halftone dots separation, gradation approximation, and vectorization. At the first module, small isolated areas and altered areas by dilation and erosion operations are separated into halftone dots images. At the second module, areas of halftone are approximated by contours and gradation parameters. At the last module, both halftone areas and line drawings are vectorized and approximated by smooth contours. The size of compressed file produced by our method is equal or smaller than JBIG compression. Validity of the proposed method is confirmed by experimental results.

No reference video quality assessment based on parametric analysis of HEVC bitstream

The quality of experience (QoE) on consumer video services must be maintained at a sufficient level. In order to realize automatic quality monitoring with a low burden, a no-reference (NR) objective video-quality-model by using a bitstream is promising because it can estimate a perceptual picture quality by referring to only parameters in the bitstream. High efficiency video coding (HEVC) is expected as the next-generation representative codec. Although parametric NR methods for AVC have been studied, such methods for HEVC are an emerging task in this research field. We propose an objective perceptual video-quality-measurement for HEVC that conforms to the parametric NR scheme.

Prediction mode based reference line synthesis for intra prediction of video coding

Intra prediction is a significant coding tool that allows a high level of video compression to be achieved in the current state-of-the-art video coding standard, High Efficiency Video Coding (HEVC), and the joint exploration model (JEM) developed by the Joint Video Exploration Team (JVET) of ITU-T VCEG and ISO/IEC MPEG for the next generation video coding standard. In intra prediction, the top and left adjacent lines to the current coding block in the neighboring reconstructed blocks are selected as the reference lines. However, it has been observed that the adjacent reference line might not always provide optimal prediction due to the quantization noise and object occlusions caused by straight lines. In this paper, we propose the synthesis of another reference line by integrating multiple lines in the neighboring reconstructed blocks based on the prediction mode. The synthesized line and the reconstructed adjacent line compete in the rate distortion optimization process, where the line that yields a minimum cost is finally selected. The proposed method is implemented on top of JEM 3.0, and the experimental results show that −0.29% (average), −1.15% (maximum) luma BD gain and −0.15% (average), −0.59% (maximum) luma BD gain can be achieved in all intra and random access conditions, respectively, among all the test sequences.

An adaptive residual decorrelation method for HEVC

In this paper, we propose an explicit residual decorrelation method to improve the coding performance for 4:4:4 chroma format conforming HEVC framework. The energy from a residual signal is gathered to the primary component by decorrelation of color space. The transform matrix as decorrelation is derived from reference pixel value by using singular value decomposition for each prediction unit. Since the derivation is applied in both encoder and decoder side, the identical matrix is obtained for both sides. Compared to the previous works, the proposed method applies only for the meaningful unit while an enabled flag is explicitly signaled as side information. The proposed method is implemented on HEVC test model. For the RGB/YUV 4:4:4 chroma format sequences, the coding gains in BD-rate are up to 23.3%/4.9%, respectively. Compared to the result by the previous works, average gains are slightly decreased, while each gain of sequence is always better than that by conventional method.

A Study on Spatial Scalable Coding using Vector Representation

The major advantage of vector representation of an image is that the image quality is maintained for arbitrary scaling. In recent years, a demand for scalable image coding has been increasing because of the wide variety of available digital contents and display terminals. Conventional scalable coding schemes are based on raster representation, and thus, line drawings deteriorate in quality when expanded and shrunk. In this paper, we propose an edge reconstruction method using vector representation for the purpose of keeping a consistent spatial scalability on transmission and display. We take an anti-aliasing into account in edge areas for approximation of luminance values around the edge. The proposed method can improve PSNR by up to 2 dB as compared to the conventional methods when image is expanded and shrunk

Bit Rate Reduction of Vector Representation of Binary Images

Vector representation of binary images has an advantage of keeping high image quality for arbitrary scaling as well as editing capability of an object. However, the vector representation suffers from low compression efficiency compared with JBIG. In this paper, we show the main cause reducing coding efficiency and propose two methods to improve it. The proposed methods can reduce the file size of a binary image up to about 30-40 percent.