Scan Format Conversion Research Articles

Locally estimated heterogeneity property and its fuzzy filter application for deinterlacing

This paper presents an intra-field scanning format conversion method using two filters: bilinear filter (BF) and fuzzy-based weighted average filter (FWAF). The proposed method is intended for black and white images, luminance component of YIQ color space, or each color component of RGB color space. We start from the notion that pixels to be interpolated can be classified into two areas based on local variance: homogeneous and heterogeneous areas. According to the local variance criteria, we apply the FWAF to the heterogeneous area and the BF to the homogeneous one, producing good visual results. Our FWAF consists of an intensity similarity filter and a geometric closeness filter. The latter is used to populate the heterogeneous area with the missing lines, due to its high deinterlacing precision. Our experimental results show that the proposed approach provides satisfactory performances in terms of both objective metrics and visual image quality. We used parameter tuning on our training set to explore the relationship between objective quality and computational complexity. We report on how to achieve good performance or the best quality-speed tradeoff using the methods researched.

A Hybrid Algorithm Using Maximum a Posteriori for Interlaced to Progressive Scanning Format Conversion

This paper presents a maximum a posteriori (MAP) based intra-field deinterlacing algorithm. In the proposed algorithm, we propose a hybrid approach composed by point-wise and patch-wise measurements. The estimation of the missing pixel is formulated as an MAP and minimizing the energy function. By utilizing Bayes theory and some prior knowledge, the missing pixel is estimated with a statistical-based approach and we model the residual of the images as Gaussian and Laplacian distribution. Under the MAP framework, the desired deinterlaced image corresponds to the optimal reconstruction given the interlaced low resolution image. Compared with existing deinterlacing algorithms, the proposed algorithm improves peak signal-to-noise-ratio and the structural similarity while maintaining high efficiency.

Fuzzy Metric Based Weight Assignment for Deinterlacing

An effective interlaced-to-progressive scanning format conversion method is presented for the interpolation of interlaced images. On the basis of the weight assignment algorithm, the proposed method is composed of three stages: (1) straightforward interpolation with pre-determined six-tap filter, (2) fuzzy metric-based weight assignment, (3) updating the interpolation results. We first deinterlace the missing line with six-tap filter in the working window. Then we compute the local weight among the adjacent pixels with a fuzzy metric. Finally we deinterlace the missing pixels using the proposed interpolator. Comprehensive simulations conducted on different images and video sequences have proved the effectiveness of the proposed method, with significant improvement over conventional methods.

Moving Least-Squares Method for Interlaced to Progressive Scanning Format Conversion

In this paper, we introduce an efficient intra-field deinterlacing algorithm based on moving least squares (MLS). The MLS algorithm has proven successful for approximating scattered data by minimizing a weighted mean-square error norm. In order to estimate the value of the missing point using the given data, we utilize MLS to generate a generic local approximation function about this point. In the MLS method, we adopt trigonometric functions to approximate the local function. This method is compared to other benchmark algorithms in terms of peak signal-to-noise ratio and structural similarity objective quality measures and deinterlacing speed. It was found to provide excellent performance and the best quality-speed tradeoff among the methods studied.

Filter design and its application for scanning format conversion

This letter presents the issue of interpolating missing line components at the interlaced signals, a procedure called deinterlacing. A filter design method is introduced in detail, utilizing a least-squares algorithm with training set for the requested deinterlacing filter. A thorough research of deinterlacing performance and system complexity is executed. The proposed filter design and its application on deinterlacing is compared with other conventional filter-based deinterlacing methods in terms of quality measures. It was concluded to show outstanding performance and speed trade-off among the methods compared.

Motion Compensated Deinterlacing With Efficient Artifact Detection for Digital Television Displays

Deinterlacing is a scanning-format conversion technique that can convert interlaced pictures to progressive ones. Progressive televisions or monitors intended for displaying conventional interlaced programs have to apply deinterlacing techniques as required. Motion adaptive deinterlacing has been used widely in various kinds of commercial products, but the picture quality in motion areas still has much room for improvement. Motion compensated deinterlacing solves this problem by interpolation along the motion trajectory and thus is becoming the main trend of the next generation of deinterlacing algorithms. However, it suffers visible artifacts caused by incorrect motion vectors. To improve such imperfect pictures, this paper presents a motion compensation algorithm with more accurate motion estimation and more efficient artifact detection for digital television displays. Soft-switching between intra and inter interpolations is realized by characterizing different areas of the picture. Simulation results indicate that the proposed scheme can produce high-quality pictures with less flicker and imperceptible artifacts than a number of other techniques that were tested. In particular, the simulated peak signal-to-noise ratio (peak SNR) is basically 3-10 dB better than a comparable and prominent deinterlacing method.

Discrete cosine transform coefficients based scan format conversion

The extendible inverse discrete cosine transform (DCT) algorithm is introduced in this paper. It is employed in scan format conversion (SFC) of digital video, and its application is extended to decimation. The traditional decimation scheme is discussed and its defects are outlined. Comparison with the bilinear and bicubic spline algorithms shows that the visual quality of the extendible inverse discrete cosine transform (EIDCT) algorithm is equivalent to these two methods. Furthermore, it is more cost effective in hardware implementation, closer to real-time processing and lower in power consumption.

P‐28: Digital Zone Control for Geometry, Convergence, and Uniformity Correction

AbstractA simplified approach to waveform generation and scan conversion is described. Wave tables are used to store correction waveforms for Geometry, Convergence and Uniformity. Over‐sampling is used to restore the original wave shape from the tables. Scan format conversion is separate from waveform generation. Raster line numbers are ‘up’ converted to scan address for the over‐sampler.

A display processor conforming to all DTV formats with 188-tap FIR filters and 284 Kb FIFO memories

To achieve a single chip solution for a display processor conforming to all DTV formats, various hardwired approaches such as the write end toggle signal (WETS) based design technique and dynamic voltage sensing FIFO architecture have been developed. As a result, all functions including macroblock-to-raster conversion, frame/filed rate conversion, scan format conversion, and ordinary picture making functions such as color interpolation, enhancement, inverse matrix, on screen display, and D/A conversion have been successfully integrated into a single chip. The display processor has a total memory capacity of 284 Kb and filters with a total of 188 taps in an area of 14.9 mm/spl times/14.9 mm. It was fabricated in 0.5 um CMOS technology with 2-metal.