Quantization (image processing)

About: Quantization (image processing) is a research topic. Over the lifetime, 7977 publications have been published within this topic receiving 126632 citations.

Bit-rate control using piecewise approximated rate-distortion characteristics

Abstract: Digital video's increased popularity has been driven to a large extent by a flurry of international standards (MPEG-1, MPEG-2, H.263, etc). In most standards, the rate control scheme, which plays an important role in improving and stabilizing the decoding and playback quality, is not defined, and thus different strategies can be implemented in each encoder design. Several rate-distortion (R-D)-based techniques have been proposed aimed at the best possible quality for a given channel rate and buffer size. These approaches are complex because they require the R-D characteristics of the input data to be measured before making quantization assignment decisions. We show how the complexity of computing the R-D data can be reduced without significantly reducing the performance of the optimization procedure. We propose two methods which provide successive reductions in complexity by: (1) using models to interpolate the rate and distortion characteristics, and (2) using past frames instead of current ones to determine the models. Our first method is applicable to situations (e.g., broadcast video) where a long encoding delay is possible, while our second approach is more useful for computation-constrained interactive video applications. The first method can also be used to benchmark other approaches. Both methods can achieve over 1 dB peak signal-to-noise rate (PSNR) gain over simple methods like the MPEG Test Model 5 (TM5) rate control, with even greater gains during scene change transitions. In addition, both methods make few a priori assumptions and provide robustness in their performance over a range of video sources and encoding rates. In terms of complexity, our first algorithm roughly doubles the encoding time as compared to simpler techniques (such as TM5). However, the complexity is greatly reduced as compared to methods which exactly measure the R-D data. Our second algorithm has a complexity marginally higher than TM5 and a PSNR performance slightly lower than that of the first approach.

Detection of Double-Compression in JPEG Images for Applications in Steganography

Abstract: This paper presents a method for the detection of double JPEG compression and a maximum-likelihood estimator of the primary quality factor. These methods are essential for construction of accurate targeted and blind steganalysis methods for JPEG images. The proposed methods use support vector machine classifiers with feature vectors formed by histograms of low-frequency discrete cosine transformation coefficients. The performance of the algorithms is compared to selected prior art.

Luminance-model-based DCT quantization for color image compression

Abstract: A model is developed to approximate visibility thresholds for discrete cosine transform (DCT) coefficient quantization error based on the peak-to-peak luminance of the error image. Experimentally measured visibility thresholds for R, G, and B DCT basis functions can be predicted by a simple luminance-based detection model. This model allows DCT coefficient quantization matrices to be designed for display conditions other than those of the experimental measurements: other display luminances, other veiling luminances, and other spatial frequencies (different pixel spacings, viewing distances, and aspect ratios).

The quadratic-chi histogram distance family

Abstract: We present a new histogram distance family, the Quadratic-Chi (QC) QC members are Quadratic-Form distances with a cross-bin χ2-like normalization The cross-bin χ2-like normalization reduces the effect of large bins having undo influence Normalization was shown to be helpful in many cases, where the χ2 histogram distance outperformed the L2 norm However, χ2 is sensitive to quantization effects, such as caused by light changes, shape deformations etc The Quadratic-Form part of QC members takes care of cross-bin relationships (eg red and orange), alleviating the quantization problem We present two new crossbin histogram distance properties: Similarity-Matrix-Quantization-Invariance and Sparseness-Invariance and show that QC distances have these propertiesWe also show that experimentally they boost performance QC distances computation time complexity is linear in the number of non-zero entries in the bin-similarity matrix and histograms and it can easily be parallelizedWe present results for image retrieval using the Scale Invariant Feature Transform (SIFT) and color image descriptors In addition, we present results for shape classification using Shape Context (SC) and Inner Distance Shape Context (IDSC) We show that the new QC members outperform state of the art distances for these tasks, while having a short running time The experimental results show that both the cross-bin property and the normalization are important

Distortion-Free Data Embedding for Images

Abstract: One common drawback of virtually all current data embedding methods is the fact that the original image is inevitably distorted by some small amount of noise due to data embedding itself. This distortion typically cannot be removed completely due to quantization, bit-replacement, or truncation at the grayscales 0 and 255. Although the distortion is often quite small, it may not be acceptable for medical imagery (for legal reasons) or for military images inspected under unusual viewing conditions (after filtering or extreme zoom). In this paper, we introduce a general approach for high-capacity data embedding that is distortion-free (or lossless) in the sense that after the embedded information is extracted from the stego-image, we can revert to the exact copy of the original image before the embedding occurred. The new method can be used as a powerful tool to achieve a variety of non-trivial tasks, including distortion-free robust watermarking, distortion-free authentication using fragile watermarks, and steganalysis. The proposed concepts are also extended to lossy image formats, such as the JPG.