Gregory S. Yovanof

Noise estimation and filtering using block-based singular value decomposition

Preprocessing of image and video sequences with spatial filtering techniques usually improves the image quality and compressibility. We present a block-based, nonlinear filtering algorithm based on singular value decomposition and compression-based filtering. Experiments show that the proposed filter preserves edge details and can significantly improve the compression performance.

Statistical analysis of the DCT coefficients and their quantization error

A detailed probabilistic analysis of the DCT coefficients and the error incurred during the quantization and reconstruction of natural images is presented. The distribution of both the DCT coefficients and their quantization error were modeled with the generalised Gaussian function (GGF) which includes the Gaussian and Laplacian PDF as special cases. The /spl chi//sup 2/ and Kolmogorov-Smirnov goodness-of-fit tests were used to determine the free parameter of the GGF that yields the best fit to experimental data. This analysis has yielded several new results regarding the distribution of the coefficients, their differences and their quantization error. The results of this analysis can improve the design of practical DCT based compression systems.

A simple block-based lossless image compression scheme

A novel low-complexity lossless scheme for continuous-tone images dubbed the PABLO codec (Pixel And Block adaptive LOw complexity coder) is introduced. It comprises a simple pixel-wise adaptive predictor and a block-adaptive coder based on the Golomb-Rice coding method. PABLO is an asymmetric algorithm requiring no coding dictionary and only a small amount of working memory on the encoder side. Due to the simplistic data structure for the compressed data, the decoder is even simpler lending itself to very fast implementations. Experimental results show the efficiency of the proposed scheme when compared against other state-of-the-art compression systems of considerably more complexity.

Application of SVD based spatial filtering to video sequences

Video capture devices, such as CCD cameras, are a significant source of noise in image sequences. Preprocessing of video sequences with spatial filtering techniques usually improves their compressibility. We present a block-based, non-linear filtering algorithm based on the theories of SVD and compression-based filtering. A novel noise estimation algorithm allows us to operate on the input data without any prior knowledge of either the noise or signal characteristics. Experiments with real video sequences and an MPEG codec have shown that SVD based filters preserve edge details and can significantly improve nearly-lossless compression ratios by 15%.

Improved compression performance using singular value decomposition (SVD)-based filters for still images

It is well known that random noise on images significantly affects the efficiency of compression algorithms. Traditional spectral filtering techniques are effective in many cases but may require some prior knowledge of the noise and image characteristics. Furthermore, the processing requirements of spectral filters strongly depend on their noise rejection properties. In this paper we present a block-based, non-linear, filtering technique based on the Singular Value Decomposition (SVD). Traditional applications of SVD to image processing rely on heuristics to estimate the noise power and are usually applied to the entire image. The proposed scheme employs a complexity-theoretical criterion for noise estimation which exploits the well known property that random noise is hard to compare. By combining SVD with a lossless compression algorithm, in our case lossless JPEG, we can estimate the noise power and derive accurate SVD thresholds for noise removal. Simulation results on grayscale images contaminated by additive noise show that the technique can effectively filter noisy images and improve compression performance with no prior knowledge of either the image or the noise characteristics. Furthermore, the technique does not cause any blurring, unlike linear filtering techniques or median filtering.

Two-pass rate-constrained still image compression

A number of applications employing lossy compression require that a mechanism be employed to control the size of a compressed image so that it does not exceed the capacity of a fixed-size buffer. This situation arises in digital cameras, where a user expects to store a predefined number of pictures into a fixed-size buffer, or in computer peripheral systems like a laser printer where a fixed-size buffer is used to store the rendered page image. We describe a fully JPEG compliant tow-pass scheme that can compress an arbitrary image to a predetermined fixed size file. The advantages of the proposed method are that it is fully compliant with the JPEG standard image compression algorithm and it requires a smaller working buffer than other rate- control schemes.

Compression in a printer pipeline

The role of data compression within a printer pipeline is studied. Hardcopy image compression poses a unique challenge due to the presence of halftoned data used to render continuous tone images on limited resolution devices. We present a quantitative comparison of the compressibility of several halftone patterns. The performance of a variety of lossless and lossy compression algorithms is evaluated. We also address the effect that certain image processing operations, like scaling and enhancement, have on the compression performance.