Image compression with adaptive local cosines: a comparative study

The goal of this work is twofold. First, we demonstrate that an advantage can be gained by using local cosine bases over wavelets to encode images that contain periodic textures. We designed a coder that outperforms one of the best wavelet coders on a large number of images. The coder finds the optimal segmentation of the image in terms of local cosine bases. The coefficients are encoded using a scalar quantizer optimized for Laplacian distributions. This new coder constitutes the first concrete contribution of the paper. Second, we used our coder to perform an extensive comparison of several optimized bells in terms of rate-distortion and visual quality for a large collection of images. This study provides for the first time a rigorous evaluation in realistic conditions of these bells. Our experiments show that bells that are designed to reproduce exactly polynomials of degree 1 resulted in the worst performance in terms of the PSNR. However, a visual inspection of the compressed images indicates that these bells often provide reconstructed images with very few visual artifacts, even at low bit rates. The bell with the most narrow Fourier transform gave the best results in terms of the PSNR on most images. This bell tends however to create annoying visual artifacts in very smooth regions at low bit rate.     

Low-rate and flexible image coding with redundant representations.

New breakthroughs in image coding possibly lie in signal decomposition through nonseparable basis functions that can efficiently capture edge characteristics, present in natural images. The work proposed in this paper provides an adaptive way of representing images as a sum of two-dimensional features. It presents a low bit-rate image coding method based on a matching pursuit (MP) expansion, over a dictionary built on anisotropic refinement and rotation of contour-like atoms. This method is shown to provide, at low bit rates, results comparable to the state of the art in image compression, represented here by JPEG2000 and SPIHT, with generally a better visual quality in the MP scheme. The coding artifacts are less annoying than the ringing introduced by wavelets at very low bit rate, due to the smoothing performed by the basis functions used in the MP algorithm. In addition to good compression performances at low bit rates, the new coder has the advantage of producing highly flexible streams. They can easily be decoded at any spatial resolution, different from the original image, and the bitstream can be truncated at any point to match diverse bandwidth requirements. The spatial adaptivity is shown to be more flexible and less complex than transcoding operations generally applied to state of the art codec bitstreams. Due to both its ability for capturing the most important parts of multidimensional signals, and a flexible stream structure, the image coder proposed in this paper represents an interesting solution for low to medium rate image coding in visual communication applications.     

采用超分辨率重建提升压缩图像质量的方法

针对JPEG的中低码率压缩图像即高压缩率图像存在较严重的块效应以及量化噪声,提出了一种对JPEG标准压缩图像进行优化的重建-采样方法.该方法对JPEG压缩图像采用三维块匹配算法(BM3D)进行去噪,去除图像中存在的块效应和量化噪声,进而提高超分辨率重建的映射准确性,再使用外部库对去噪后图像进行基于稀疏表示的超分辨率重建,补充一定的高频信息,最后对重建后的高分辨率图进行双三次下采样,得到与原始图像大小一致的图像作为最终优化图像.实验结果表明,该方法在中低码率情况下能够有效地提高JPEG压缩图像的质量,对高码率压缩图像也有一定效果.     

基于分段式拟合的低比特率图像压缩编码方法

为了提高图像在低比特率条件下的解码质量和视觉效果,该文提出一种基于稀疏分解的低比特率图像压缩编码新方法。利用二维不可分离、具有各向异性尺度的墨西哥草帽小波作为生成函数,这种函数构建的冗余字典能够有效捕获图像边缘轮廓特征。为降低原子投影系数的冗余度和减小其编码量,对图像稀疏分解投影的系数采用分段式拟合。获得的图像压缩码流具有渐进特性的,满足现代无线通信对可伸缩码流的要求。实验结果表明,在低比特率下,该文方法与JPEG2000方法和通常的稀疏分解编码法相比,有更高的峰值信噪比;解压图像无振铃效应,主观效果好。     

Medical image compression by discrete cosine transform spectralsimilarity strategy

Due to bandwidth and storage limitations, medical images must be compressed before transmission and storage. However, the compression reduces the image fidelity, especially when the images are compressed at low bit rates. The reconstructed images suffer from blocking artifacts and the image quality is severely degraded under high compression ratios. In this paper, we present a strategy to increase the compression ratio with low computational burden and excellent decoded quality. We regard the discrete cosine transform as a bandpass filter to decompose a sub-block into equal-sized bands. After a band-gathering operation, a high similarity property among the bands is found. By utilizing the similarity property, the bit rate of compression can be greatly reduced. Meanwhile, the characteristics of the original image are not sacrificed. Thus, it can avoid the misdiagnosis of diseases. Simulations were carried out on different kinds of medical images to demonstrate that the proposed method achieves better performance when compared to other existing transform coding schemes, such as JPEG, in terms of bit rate and quality. For the case of angiogram images, the peak signal-to-noise-ratio gain is 13.5 dB at the same bit rate of 0.15 bits per pixel when compared to the JPEG compression. As for the other kinds of medical images, their benefits are not so obvious as for angiogram images; however, the gains for them are still 4-8 dB at high compression ratios. Two doctors were invited to verify the decoded image quality; the diagnoses of all the test images were correct when the compression ratios were below 20     

Predictive Coding of Speech at Low Bit Rates

Predictive coding is a promising approach for speech coding. In this paper, we review the recent work on adaptive predictive coding of speech signals, with particular emphasis on achieving high speech quality at low bit rates (less than 10 kbits/s). Efficient prediction of the redundant structure in speech signals is obviously important for proper functioning of a predictive coder. It is equally important to ensure that the distortion in the coded speech signal be perceptually small. The subjective loudness of quantization noise depends both on the short-time spectrum of the noise and its relation to the short-time spectrum of the Speech signal. The noise in the formant regions is partially masked by the speech signal itself. This masking of quantization noise by speech signal allows one to use low bit rates while maintaining high speech quality. This paper will present generalizations of predictive coding for minimizing subjective distortion in the reconstructed speech signal at the receiver. The quantizer in predictive coders quantizes its input on a sample-by-sample basis. Such sample-by-sample (instantaneous) quantization creates difficulty in realizing an arbitrary noise spectrum, particularly at low bit rates. We will describe a new class of speech coders in this paper which could be considered to be a generalization of the predictive coder. These new coders not only allow one to realize the precise optimum noise spectrum which is crucial to achieving very low bit rates, but also represent the important first step in bridging the gap between waveform coders and vocoders without suffering from their limitations.     

Image deblocking via shape-adaptive low-rank prior and sparsity-based detail enhancement

At low bit rates, visually annoying blocking artifacts are usually introduced in JPEG compressed images. In this paper, we proposed an image deblocking method combined with the shape-adaptive low-rank (SALR) prior, the quantization constraint (QC) prior and sparsity-based detail enhancement. We firstly design a deblocking model to obtain initial deblocked images under the maximum a posteriori (MAP) framework. More specifically, with the assumption of Gaussian quantization noise, the SALR prior is utilized to effectively separate signal from noise and preserve image edges. Compared with previous low rank priors, the SALR reconstructs a better result via shape adaptive blocks. The QC prior is also adopted to avoid over-smoothing and to enable a more accurate estimation. Finally, by extracting features of external images, the mapping matrix of sparse dictionary pairs is trained to enhance image details. Extensive experimental results demonstrate that the proposed deblocking method has superior performances in both subjective vision and objective quality.     

A locally adaptive algorithm for measuring blocking artifacts in images and videos

Block transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring image quality of a BDCT coded images or videos. It exhibits unique and useful features: (1) it examines the blocks individually so that it can measure the severity of blocking artifacts locally; (2) it is a one-pass algorithm in the sense that the image needs to be accessed only once; (3) it takes into account the blocking artifacts for high bit rate images and the flatness for the very low bit rate images; (4) the quality measure is well defined in the range of 0–10. Experiments on various still images and videos show that the new quality measure is very efficient in terms of computational complexity and memory usage, and can produce consistent blocking artifacts measurement.     

A Pinned Sine Transform Image Coder

The pinned sine transform (PST) coder described is a practical approximation to the pinned KLT coder. The image is partitioned into two fields: a boundary field, which depends only on the coded block boundaries, and a pinned field, which vanishes at the boundaries and is subsequently sine transformed and compressed. The reconstructed image is continuous across the block boundaries and "blocking effects" are hardly visible. Simulation results are given for fixed and adaptive compression schemes with fair image quality at low bit rates down to 0.3 bits/pel.     

Down-scaling for better transform compression

The most popular lossy image compression method used on the Internet is the JPEG standard. JPEG's good compression performance and low computational and memory complexity make it an attractive method for natural image compression. Nevertheless, as we go to low bit rates that imply lower quality, JPEG introduces disturbing artifacts. It is known that, at low bit rates, a down-sampled image, when JPEG compressed, visually beats the high resolution image compressed via JPEG to be represented by the same number of bits. Motivated by this idea, we show how down-sampling an image to a low resolution, then using JPEG at the lower resolution, and subsequently interpolating the result to the original resolution can improve the overall PSNR performance of the compression process. We give an analytical model and a numerical analysis of the down-sampling, compression and up-sampling process, that makes explicit the possible quality/compression trade-offs. We show that the image auto-correlation can provide a good estimate for establishing the down-sampling factor that achieves optimal performance. Given a specific budget of bits, we determine the down-sampling factor necessary to get the best possible recovered image in terms of PSNR.     

Strategies to improve the performance of very low bit rate speech coders and application to a variable rate 1.2 kb/s codec

This paper presents several strategies to improve the performance of very low bit rate speech coders and describes a speech codec that incorporates these strategies and operates at an average bit rate of 1.2 kb/s. The encoding algorithm is based on several improvements in a mixed multiband excitation (MMBE) linear predictive coding (LPC) structure. A switched-predictive vector quantiser technique that outperforms previously reported schemes is adopted to encode the LSF parameters. Spectral and sound specific low rate models are used in order to achieve high quality speech at low rates. An MMBE approach with three sub-bands is employed to encode voiced frames, while fricatives and stops modelling and synthesis techniques are used for unvoiced frames. This strategy is shown to provide good quality synthesised speech, at a bit rate of only 0.4 kb/s for unvoiced frames. To reduce coding noise and improve decoded speech, spectral envelope restoration combined with noise reduction (SERNR) postfilter is used. The contributions of the techniques described in this paper are separately assessed and then combined in the design of a low bit rate codec that is evaluated against the North American Mixed Excitation Linear Prediction (MELP) coder. The performance assessment is carried out in terms of the spectral distortion of LSF quantisation, mean opinion score (MOS), A/B comparison tests and the ITU-T P.862 perceptual evaluation of speech quality (PESQ) standard. Assessment results show that the improved methods for LSF quantisation, sound specific modelling and synthesis and the new postfiltering approach can significantly outperform previously reported techniques. Further results also indicate that a system combining the proposed improvements and operating at 1.2 kb/s, is comparable (slightly outperforming) a MELP coder operating at 2.4 kb/s. For tandem connection situations, the proposed system is clearly superior to the MELP coder.     

Compression of color facial images using feature correctiontwo-stage vector quantization

A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better image quality than KLT-FC2VQ or YCbCr-FC2VQ at similar bit rates. With the RGB-FC2VQ algorithm, a 128x128 24-b color ID image (49152 bytes) can be compressed down to about 500 bytes with satisfactory quality. When the codeword indices are further compressed losslessly using a first order Huffman coder, this size is further reduced to about 450 bytes.     

A new image quality assessment method to detect and measure strength of blocking artifacts

Block based transform coding is one of the most popular techniques for image and video compression. However it suffers from several visual quality degradation factors, most notably from blocking artifacts. The subjective picture quality degradation caused by blocking artifacts, in general, does not agree well with the popular objective quality measure such as PSNR.A new image quality assessment method that detects and measures strength of blocking artifacts for block based transform coded images is proposed. In order to characterize the blocking artifacts, we utilize two observations: if blocking artifacts occur on the block boundary, the pixel value changes abruptly across the boundary and the same pixel values usually span along the entire length of the boundary. The proposed method operates only on a single block boundary to detect blocking artifacts. When a boundary is classified as having blocking artifacts, corresponding blocking artifact strength is also computed. Average values of those blocking artifact strengths are converted into a single number representing the subjective image quality. Experiments on various JPEG compressed images with various bit rates demonstrated that the proposed blocking artifacts measuring value matches well with the subjective image quality judged by human observers.     

Lossy compression of noisy images

Noise degrades the performance of any image compression algorithm. This paper studies the effect of noise on lossy image compression. The effect of Gaussian, Poisson, and film-grain noise on compression is studied. To reduce the effect of the noise on compression, the distortion is measured with respect to the original image not to the input of the coder. Results of noisy source coding are then used to design the optimal coder. In the minimum-mean-square-error (MMSE) sense, this is equivalent to an MMSE estimator followed by an MMSE coder. The coders for the Poisson noise and the film-grain noise cases are derived and their performance is studied. The effect of this preprocessing step is studied using standard coders, e.g., JPEG, also. As is demonstrated, higher quality is achieved at lower bit rates.     

Variable bit rate adaptive predictive coder

An adaptive predictive coder providing almost toll quality at 16 kb/s and minimal degradation when the bit rate is lowered to 9.6 kb/s is described. The coder can operate at intermediate bit rates and can also change bit rate on a packet-by-packet basis. Variable bit rate operation is achieved through the use of switched quantization, thus eliminating the need for buffering of the output. A noise shaping filter provides flexible control of the output noise spectrum. The filter, in conjunction with an enhanced way to adapt the quantizer step size, which tries to accommodate the quantization noise feedback, accounts for the toll quality. By quantizing the residue with more than one quantizer, the effective number of bits per sample can be controlled in a deterministic way regardless of the entropy residue. The lower limit of operation is at 9.6 kb/s. Performance of the coder under random bit errors is also presented. It has been found that only at error rates of 10^-2 and higher does the degradation becomes objectionable     

Artifact reduction in JPEG2000 compressed images at low bit-rate using mathematical morphology filtering

JPEG2000 is known as an efficient standard to encode images. However, at very low bit-rates, artifacts or distortions can be observed in decoded images. In order to improve the visual quality of decoded images and make them perceptually acceptable, we propose in this work a new preprocessing scheme. This scheme consists in preprocessing the image to be encoded using a nonlinear filtering, considered as a prior phase to JPEG 2000 compression. More specifically, the input image is decomposed into low- and high-frequency sub-images using morphological filtering. Afterward, each sub-image is compressed using JPEG2000, by assigning different bit-rates to each sub-image. To evaluate the quality of the reconstructed image, two different metrics have been used, namely (a) peak signal to noise ratio, to evaluate the visual quality of the low-frequency sub-image, and (b) structural similarity index measure, to evaluate the visual quality of the high-frequency sub-image. Based on the reconstructed images, experimental results show that, at low bit-rates, the proposed scheme provides better visual quality compared to a direct use of JPEG2000 (excluding any preprocessing).     

Correction of MR kappa-space data corrupted by spike noise

Magnetic resonance images are reconstructed from digitized raw data, which are collected in the spatial-frequency domain (also called kappa-space). Occasionally, single or multiple data points in the k-space data are corrupted by spike noise, causing striation artifacts in images. Thresholding methods for detecting corrupted data points can fail because of small alterations, especially for data points in the low spatial frequency area where the k-space variation is large. Restoration of corrupted data points using interpolations of neighboring pixels can give incorrect results. We propose a Fourier transform method for detecting and restoring corrupted data points using a window filter derived from the striation-artifact structure in an image or an intermediate domain. The method provides an analytical solution for the alteration at each corrupted data point. It can effectively restore corrupted kappa-space data, removing striation artifacts in images, provided that the following three conditions are satisfied. First, a region of known signal distribution (for example, air background) is visible in either the image or the intermediate domain so that it can be selected using a window filter. Second, multiple spikes are separated by the full-width at half-maximum of the point spread function for the window filter. Third, the magnitude of a spike is larger than the minimum detectable value determined by the window filter and the standard deviation of kappa-space random noise.     

Source coding and transmission of HDTV images compressed with thewavelet transform

Digital high-definition TV (HDTV) signals are generally compressed to reduce transmission bandwidth requirements. A compression algorithm for the bit rate reduction of an HDTV image using the wavelet transform is presented. The major problems related to the transmission of a compressed HDTV signal are analyzed. Transmission is examined both on a noisy channel and an asynchronous transfer mode (ATM) network. The effects of channel noise on the reconstructed image are determined, and a solution to mitigate the degradation of the image quality is presented. A model for the output bit rate of the HDTV coder is derived and used to simulate the transmission of an ATM multiplexer so that the network's main performance parameters can be determined     

Vector-quantised transform coder for speech coding at 9-6 kbit/s and below

The transform approach to speech coding has been established for some time, and has been shown to be very efficient in controlling the bit allocation and the shape of the noise spectrum. Various transform coders have been reported which produce high-quality digital speech at around 16 kbit/s. Although these coders can maintain good quality down to about 9.6 kbit/s, they perform poorly at lower bit rates. Here we discuss how vector quantisation (VQ) can be used to improve the quality of transform coders. We describe one specific design of vector-quantised transform coder (VQTC) which follows on from earlier work, and which is capable of producing good-quality speech at as low as 4.8 kbit/s.