Ricardo L. de Queiroz

Mixed Raster Content (MRC) Model for Compound Image Compression

This paper will describe the Mixed Raster Content (MRC) method for compressing compound images, containing both binary test and continuous-tone images. A single compression algorithm that simultaneously meets the requirements for both text and image compression has been elusive. MRC takes a different approach. Rather than using a single algorithm, MRC uses a multi-layered imaging model for representing the results of multiple compression algorithms, including ones developed specifically for text and for images. As a result, MRC can combine the best of existing or new compression algorithms and offer different quality-compression ratio tradeoffs. The algorithms used by MRC set the lower bound on its compression performance. Compared to existing algorithms, MRC has some image-processing overhead to manage multiple algorithms and the imaging model. This paper will develop the rationale for the MRC approach by describing the multi-layered imaging model in light of a rate-distortion trade-off. Results will be presented comparing images compressed using MRC, JPEG and state-of-the-art wavelet algorithms such as SPIHT. MRC has been approved or proposed as an architectural model for several standards, including ITU Color Fax, IETF Internet Fax, and JPEG 2000.

A simple reversed-complexity Wyner-Ziv video coding mode based on a spatial reduction framework

A spatial-resolution reduction based framework for incorporation of a Wyner-Ziv frame coding mode in existing video codecs is presented, to enable a mode of operation with low encoding complexity. The core Wyner-Ziv frame coder works on the Laplacian residual of a lower-resolution frame encoded by a regular codec at reduced resolution. The quantized transform coefficients of the residual frame are mapped to cosets to reduce the bit-rate. A detailed rate-distortion analysis and procedure for obtaining the optimal parameters based on a realistic statistical model for the transform coefficients and the side information is also presented. The decoder iteratively conducts motion-based side-information generation and coset decoding, to gradually refine the estimate of the frame. Preliminary results are presented for application to the H.263+ video codec.

Fast segmentation of the JPEG compressed documents

We present a novel technique for segmentation of a JPEG-compressed document based on block activity. The activity is measured as the number of bits spent to encode each block. Each number is mapped to a pixel brightness value in an auxiliary image which is then used for segmentation. We introduce the use of such an image and show an example of a simple segmentation algorithm, which was successfully applied to test documents. The document is segmented into characteristics regions labeled as background, half- tones, text, graphics, and continuous tone images. The key feature of the proposed framework is that the desired region can be identi- fied and cropped (or removed) from the compressed data without decompressing the image.

Mobile video communications using a Wyner-Ziv transcoder

In mobile-to-mobile video communications, both the transmitting and receiving ends may not have the necessary computing power to perform complex video compression and decompression tasks. Traditional video codecs typically have highly complex encoders and less complex decoders. However, Wyner-Ziv (WZ) coding allows for a low complexity encoder at the price of a more complex decoder. We propose a video communication system where the transmitter uses a WZ (reversed complexity) coder, while the receiver uses a traditional decoder, hence minimizing complexity at both ends. For that to work we propose to insert a transcoder in the network to convert the video stream. We present an efficient transcoder from a simple WZ approach to H.263. Our approach saves a large amount of the computation by reusing the motion estimation performed at the WZ decoder stage, among other things. Results are presented to demonstrate the transcoder performance.

A neural network approach to key frame extraction

We present a neural network based approach to key frame extraction in the compressed domain. The proposed method is an amalgamation of both the MPEG-7 descriptors namely motion intensity descriptor and spatial activity descriptor. Shot boundary detection and block motion estimation techniques are employed prior to the extraction of the descriptors. The motion intensity (“pace of action”) is obtained using a fuzzy system that classifies the motion intensity into five categories proportional to the intensity. The spatial activity matrix determines the spatial distribution of activity (“active regions”) in a frame. A neural network is used to pick those frames as key frames which have high intensity and maximum spatial activity at the center of the frame. Results are compared against two well-known key frame extraction techniques to demonstrate the advantage and robustness of the proposed approach. Results show that the neural network approach performs much better than selecting first frame of the shot as a key frame and selecting middle frame of the shot as a key frame methods.

Universal descreening technique via wavelet analysis

In this paper, a novel wavelet-based approach to recover continuous tone images from halftone images is presented. Wavelet decomposition of the halftone image facilitates a series of spatial and frequency selective processing to preserve most of the original image contents while eliminating the halftone noise. Furthermore, optional non- linear filtering can be applied as post-processing stage to create the final aesthetic contone image. This approach lends itself to practical applications since it is independent of parameter estimation and hence universal to all types of halftoned images, including those obtained by scanning printed halftones.

Fast-lapped transform for image coding

This paper introduces a class of linear phase lapped biorthogonal transforms with basis functions of variable length. A lattice is used to enforce both linear phase and perfect reconstruction properties as well as to provide a fast and efficient transform implementation for image coding applications. In the proposed formulation which we call fast lapped transform (FLT), the higher frequency filters (basis functions) are those of the DCT, which are compact to limit ringing. The lower frequency filters (basis functions) are overlapped for representing smooth signals while avoiding blocking artifacts. A great part of the FLT computation is spent at the DCT stage, which can be implemented through fast algorithms, while just a few more operations are needed to implement the extra stages. For example, compared to the DCT, an FLT with good performance can be implemented with only 8 extra additions and 6 extra multiplications for an 8-sample block. Yet, image coding examples show that the FLT is far superior to the DCT and is close to the 9/7-tap biorthogonal wavelet in subjective coding performance.

Energy-constrained real-time H.264/AVC video coding

Energy consumption has become a leading design constraint for computing devices in order to defray electric bills for individuals and businesses. Over the past years, digital video communication technologies have demanded higher computing power availability and, therefore, higher energy expenditure. In order to meet the challenge to provide software-based video encoding solutions, we adopted an open source software implementation of an H.264 video encoder, the x264 encoder, and optimized its prediction stage in the energy sense (E). Thus, besides looking for the coding options which lead to the best coded representation in terms of rate and distortion (RD), we constrain the process to fit within a certain energy budget. i.e., an RDE optimization. We considered energy as the time integration of the real demanded electric power for a given system. We present an RDE-optimized framework which allows for software-based real-time video compression, meeting the desired targets of electrical consumption, hence, controlling carbon emissions. We show results of energy-constrained compression wherein one can save as much as 35% of the energy with small impact on RD performance.

Multiresolution color correction

In this paper, a color correction system is embedded into a multiresolution representation with the goal of reducing the complexity of 3D look-up table transformations. A framework is assumed wherein the image undergoes a multiresolution decomposition, e.g. discrete wavelet transform, for the purpose of image compression or other processing. After the image is reconstructed from its multiresolution representation, color correction is usually required for rendering to a specific device. The color correction process is divided into two phases: a complex multidimensional transform (Phase 1), and a series of essentially 1-D transforms (Phase 2). Phase 1 correction is then moved within the multiresolution reconstruction process in such a way that a small subset of the image samples undergoes the multidimensional correction. Phase 2 correction is then applied to all image samples after the image is reconstructed to its full resolution. The recently proposed spatial CIELAB model is used to evaluate the algorithm. The computational cost incurred by the color correction is considerably reduced, with little loss in image quality.

Improved Chen-Smith image coder

A new transform coder based on the zonal sampling strategy, which outperforms the JPEG baseline coder with comparable computational complexity, is presented. The primary transform used is the 8- × 8-pixel-block discrete cosine transform, although it can be replaced by other transforms, such as the lapped orthogonal transform, without any change in the algorithm. This coder is originally based on the Chen-Smith coder, therefore, we call it an improved Chen-Smith (ICS) coder. However, because many new features were incorporated in this improved version, it largely outperforms its predecessor. Key approaches in the ICS coder, such as a new quantizer design, arithmetic coders, noninteger bit-rate allocation, decimated variance maps, distance-based block classification, and human visual sensitivity weighting, are essential for its high performance. Image compression programs were developed and applied to several test images. The results show that the ICS performs substantially better than the JPEG coder.