Perceptually and Statistically Decorrelated Features for Image Representation: Application to Transform Coding
نویسندگان
چکیده
Transform coding consists of a scalar quantization of the features of an image representation. These features should be independent enough to justify the scalar approach. The coefficients of the commonly used DCT representation still show some dependence that may reduce its efficiency. In this work, a perceptually inspired non-linear transform is used to map the DCT into a new representation that largely reduces the statistical and perceptual relations between the coefficients thus improving the compression performance1.
منابع مشابه
Second-Order Statistical Texture Representation of Asphalt Pavement Distress Images Based on Local Binary Pattern in Spatial and Wavelet Domain
Assessment of pavement distresses is one of the important parts of pavement management systems to adopt the most effective road maintenance strategy. In the last decade, extensive studies have been done to develop automated systems for pavement distress processing based on machine vision techniques. One of the most important structural components of computer vision is the feature extraction met...
متن کاملContourlet-Based Edge Extraction for Image Registration
Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. In general, the majority of registration methods consist of the following four steps: feature extraction, feature matching, transform modeling, and finally image resampling. As the accuracy of a registration process is highly dependent to the fe...
متن کاملTransform Coding of Image Feature Descriptors
We investigate transform coding to efficiently store and transmit SIFT and SURF image descriptors. We show that image and feature matching algorithms are robust to significantly compressed features. We achieve nearperfect image matching and retrieval for both SIFT and SURF using ∼2 bits/dimension. When applied to SIFT and SURF, this provides a 16× compression relative to conventional floating p...
متن کاملThe Jpeg Image Compression Algorithm
The basis for the JPEG algorithm is the Discrete Cosine Transform (DCT) which extracts spatial frequency information from the spatial amplitude samples. These frequency components are then quantized to eliminate the visual data from the image that is least perceptually apparent, thereby reducing the amount of information that must be stored. The redundant properties of the quantized frequency s...
متن کاملImage Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کامل