Low Bit Rate Image Research Articles

Generative Refinement for Low Bitrate Image Coding Using Vector Quantized Residual

Generative Refinement for Low Bitrate Image Coding Using Vector Quantized Residual

MULTI VARIATE NEURO-STATISTICAL SPARSE TRANSFORM FOR GRAY SCALE IMAGES

Abstract-- The main objective of this paper is to examine the performance of Neuro- Statistical sparse transformation function for implementation in a still image vector coding based compression system. This paper discusses the important features of low bit-rate image coding which is based on recent developments in the theory of multivariate nonlinear piecewise polynomial approximation in still images. It combines Binary Space Partition (BSP) scheme with Geometric Wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image. The quality of the reconstructed image is measured objectively using Peak Signal to Noise Ratio. Experimental results show that the proposed image compression system yields higher compression with minimal loss.

Impact of the lossy image compression on the biometric system accuracy: a case study of hand biometrics

Biometric recognition systems are used in several cases, to recognise people using images. Storing of large images require large storage space. To reduce the storage space, compression methods are employed. In this paper, we analyse the effect of lossy image compression on the performance of biometric identification systems. We propose a scheme to evaluate the recognition performance at low bitrates of hand images. The images are compressed using set partitioning in hierarchical trees (SPIHT) encoding. A powerful feature extraction algorithm based on quantising the phase information of the local Fourier transform is used. The nearest neighbour (NN) classifier and the support vector machine (SVM) classifier are employed to classify the feature extraction. The obtained results show at the compression does not significantly affect the performance of recognition operation at low bitrate for unimodal and multimodal systems. Thus, the low bitrate images perform equivalent to uncompressed images in the recognition system.

CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks

In recent years, much research has been conducted on image super-resolution (SR). To the best of our knowledge, however, few SR methods were concerned with compressed images. The SR of compressed images is a challenging task due to the complicated compression artifacts, while many images suffer from them in practice. The intuitive solution for this difficult task is to decouple it into two sequential but independent subproblems, i.e., compression artifacts reduction (CAR) and SR. Nevertheless, some useful details may be removed in CAR stage, which is contrary to the goal of SR and makes the SR stage more challenging. In this paper, an end-to-end trainable deep convolutional neural network is designed to perform SR on compressed images (CISRDCNN), which reduces compression artifacts and improves image resolution jointly. Experiments on compressed images produced by JPEG (we take the JPEG as an example in this paper) demonstrate that the proposed CISRDCNN yields state-of-the-art SR performance on commonly used test images and imagesets. The results of CISRDCNN on real low quality web images are also very impressive, with obvious quality enhancement. Further, we explore the application of the proposed SR method in low bit-rate image coding, leading to better rate-distortion performance than JPEG.

Low-complexity low-memory energy-efficient image coding for wireless image sensor networks

ABSTRACTWireless image sensor networks are capable of sensing, processing and transmitting the image in hard-to-access regions without expensive network infrastructure and will have great contribution in Internet of Things. These networks are resource constraint systems with limited memory, energy, processing speed and bandwidth. Low computational energy and communication energy will improve the lifetime of these resource-limited networks. In this paper, an energy-efficient low-memory and low-bitrate image coding is designed exclusively for low-power camera-equipped sensor node. The performance of the proposed image coder is analysed in terms of bitrate, image quality, memory size and energy consumption. Experiments are carried out with Atmel ATmega128 processor. The experimental results show that this system consumes only 0.23% of energy consumed by true Discrete Cosine Transform-based Joint Photographic Experts Group (JPEG) standard and offers reasonable image quality suitable for visual perception at low bitrate. This system requires only 19% of memory required by standard JPEG.

LOW BITRATE HYBRID SECURED IMAGE COMPRESSION FOR WIRELESS IMAGE SENSOR NETWORK

Wireless image sensor networks are capable of sensing, processing and transmitting the visual data along with the scalar data and have attainedwide attention in sensitive applications such as visual surveillance, habitat monitoring, and ubiquitous computing. The sensor nodes in the network are resource constrained in nature. Since the image data are huge always high computational cost and energy budget are levied on the sensor nodes. The compression standards JPEG and JPEG 2000 are not feasible as they involve complex computations. To stretch out the life span of these nodes,it is required to have low complex and low bitrate image compression techniques exclusively designed for this platform. The complicated scenarioof wireless sensor network in processing and transmitting image data has been addressed by a low complex hybrid secured image compression technique using discrete wavelet transform and Bin discrete cosine transformation.

Compressibility Constrained Sparse Representation With Learnt Dictionary for Low Bit-Rate Image Compression

This paper proposes a compressibility constrained sparse representation (CCSR) approach to low bit-rate image compression using a learnt over-complete dictionary of texture patches. Conventional sparse representation approaches for image compression are based on matching pursuit (MP) algorithms. Actually, the weakness of these approaches is that they are not stable in terms of sparsity of the estimated coefficients, thereby resulting in the inferior performance in low bit-rate image compression. In comparison with MP, convex relaxation approaches are more stable for sparse representation. However, it is intractable to directly apply convex relaxation approaches to image compression, as their coefficients are not always compressible. To utilize convex relaxation in image compression, we first propose in this paper a CCSR formulation, imposing the compressibility constraint on the coefficients of sparse representation for each image patch. In addition, we work out the CCSR formulation to obtain sparse and compressible coefficients, through recursively solving the \(\ell _{1}\) -norm optimization problem of sparse representation. Given these coefficients, each image patch can be represented by the linear combination of texture elements encoded in an over-complete dictionary, learnt from other training images. Finally, low bit-rate image compression can be achieved, owing to the sparsity and compressibility of coefficients by our CCSR approach. The experimental results demonstrate the effectiveness and superiority of the CCSR approach on compressing the natural and remote sensing images at low bit-rates.

HVS-Based Low Bit-Rate Image Compression

Image coding and compression is one of the most key techniques in the area of image signal processing, However, most of the existing coding methods such as JPEG, employ the similar hybrid architecture to compress images and videos. After many years of development, it is difficult to further improve the coding performance. In addition, most of the existing image compression algorithms are designed to minimize difference between the original and decompressed images based on pixel wise distortion metrics, such as MSE, PSNR which do not consider the HVS features and is not able to guarantee good perceptual quality of reconstructed images, especially at low bit-rate scenarios. In this paper, we propose a novel scheme for low bit-rate image compression. Firstly, the original image is quantized to a binary image based on heat transfer theory. Secondly, the bit sequence of the binary image is divided into several sub-sets and each one is designated a priority based on the rate-distortion principle. Thirdly, the sub-sets with high priorities are selected based on the given bit-rate. Finally, the context-based binary arithmetic coding is employed to encode the sub-sets selected to produce the final compressed stream. At decoder, the image is decoded and reconstructed based on anisotropic diffusion. Experiments are conducted and provide convincing results.

Low complexity energy efficient very low bit-rate image compression scheme for wireless sensor network

This paper presents an extremely fast and very low complex energy efficient image compression scheme for resource constrained, low powered devices in real time applications. The proposed Optimal Zonal 2×2 BinDCT is unique since it directly extracts and codes the DC Coefficient and the first three AC coefficients of an 8×8 image block using only shift-and-add operations. It is specifically designed for the resource constrained image sensor wireless networks requiring very low bitrates and is investigated in terms of image quality, entropy, processing time and overall energy consumption. Experiments are performed using the Atmel ATmega128 processor to measure the computation time and the resultant energy savings. Simulation results show that the proposed algorithm is extremely simple, fast and consumes only 4% of computation energy needed by Independent JPEG Group IJG version and thus prolongs the system lifetime.

Hierarchical stereo matching with image bit-plane slicing

We propose a new stereo matching framework based on image bit-plane slicing. A pair of image sequences with various intensity quantization levels constructed by taking different bit-rate of the images is used for hierarchical stereo matching. The basic idea is to use the low bit-rate image pairs to compute rough disparity maps. The hierarchical matching strategy is then carried out iteratively to update the low confident disparities with the information provided by extra image bit-planes. It is shown that, depending on the stereo matching algorithms, even the image pairs with low intensity quantization are able to produce fairly good disparity results. Consequently, variate bit-rate matching is performed only regionally in the images for each iteration, and the average image bit-rate for disparity computation is reduced. Our method provides a hierarchical matching framework and can be combined with the existing stereo matching algorithms. Experiments on Middlebury datasets show that the proposed technique gives good results compared to the conventional full bit-rate matching.

A Low-bit Rate Image Compression Algorithm Based on Piecewise Matching Pursuits

A Low-bit Rate Image Compression Algorithm Based on Piecewise Matching Pursuits

A PIXEL-DOMAIN POST-PROCESSING TECHNIQUE TO REDUCE THE BLOCKING ARTIFACTS IN TRANSFORM CODED IMAGES

The reconstructed images after compression produce annoying blocking artifacts near block boundaries, particularly in low bit-rate image compression, because each block is transformed and quantized independently Detection of blocking artifact has an important role in the design of image coding systems. Increasing the bit rate to obtain better quality images is too costly. In this paper, a novel pixel-domain technique for image blocking artifact detection is presented. The algorithm detects the regions of the image which present visible blocking artifacts. The blocking artifact detection and reduction is presented as a post- processing in the image decoder. The algorithm first detects the direction of artifact through power spectrum estimation of the e pixel absolute difference (PAD) in both horizontal and vertical directions. The power spectrum of the PAD is estimated using the Maximum Entropy method (MEM) which is a nonparametric method to perform accurate power spectrum estimation. The reduction process is applied on the direction of artifacts only to improve the image quality. The key of the reduction process is based on the modified projection operator to give reasonable modifications for the block boundary pixels. The simulation results illustrate the performance of the proposed method for the JPEG decoded images.

Low Bit-Rate Image Compression via Adaptive Down-Sampling and Constrained Least Squares Upconversion

Recently, many researchers started to challenge a long-standing practice of digital photography: oversampling followed by compression and pursuing more intelligent sparse sampling techniques. In this paper, we propose a practical approach of uniform down sampling in image space and yet making the sampling adaptive by spatially varying, directional low-pass prefiltering. The resulting down-sampled prefiltered image remains a conventional square sample grid, and, thus, it can be compressed and transmitted without any change to current image coding standards and systems. The decoder first decompresses the low-resolution image and then upconverts it to the original resolution in a constrained least squares restoration process, using a 2-D piecewise autoregressive model and the knowledge of directional low-pass prefiltering. The proposed compression approach of collaborative adaptive down-sampling and upconversion (CADU) outperforms JPEG 2000 in PSNR measure at low to medium bit rates and achieves superior visual quality, as well. The superior low bit-rate performance of the CADU approach seems to suggest that oversampling not only wastes hardware resources and energy, and it could be counterproductive to image quality given a tight bit budget.

Undersampled Boundary Pre-/Postfilters for Low Bit-Rate DCT-Based Block Coders

It has been well established that critically sampled boundary pre-/postfiltering operators can improve the coding efficiency and mitigate blocking artifacts in traditional discrete cosine transform-based block coders at low bit rates. In these systems, both the prefilter and the postfilter are square matrices. This paper proposes to use undersampled boundary pre- and postfiltering modules, where the pre-/postfilters are rectangular matrices. Specifically, the prefilter is a "fat" matrix, while the postfilter is a "tall" one. In this way, the size of the prefiltered image is smaller than that of the original input image, which leads to improved compression performance and reduced computational complexities at low bit rates. The design and VLSI-friendly implementation of the undersampled pre-/postfilters are derived. Their relations to lapped transforms and filter banks are also presented. Two design examples are also included to demonstrate the validity of the theory. Furthermore, image coding results indicate that the proposed undersampled pre-/postfiltering systems yield excellent and stable performance in low bit-rate image coding.

Image Coding With Geometric Wavelets

This paper describes a new and efficient method for low bit-rate image coding which is based on recent development in the theory of multivariate nonlinear piecewise polynomial approximation. It combines a binary space partition scheme with geometric wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image. The GW method successfully competes with state-of-the-art wavelet methods such as the EZW, SPIHT, and EBCOT algorithms. We report a gain of about 0.4 dB over the SPIHT and EBCOT algorithms at the bit-rate 0.0625 bits-per-pixels (bpp). It also outperforms other recent methods that are based on "sparse geometric representation." For example, we report a gain of 0.27 dB over the Bandelets algorithm at 0.1 bpp. Although the algorithm is computationally intensive, its time complexity can be significantely reduced by collecting a "global" GW n-term approximation to the image from a collection of GW trees, each constructed separately over tiles of the image.

Mobile face detection and tracking for media streaming applications

This paper presents a method for mobile face detection and tracking in media streaming applications. To account specifics of mobile image processing and pattern recognition, the experimental benchmarking of Intel x86 and Nokia S60 platforms is done. Various aspects on mobile phone use are considered in order to record a profile on a mobile media streaming user. The profile is generated by analysing pictures of mobile phone users and screenshots of their devices. The mobile face detection model is based on the optimised version of robust real-time object detection algorithm. Our optimisation enables to reduce the computational complexity of the original version in 4 times. We also describe the tracking mode in which the system achieves a near real-time frame rate (45 12 fps depending on the video complexity). The training of the face detector is done on the databases of more than 400 hr of video and the major parameters such as FRR and FAR are evaluated on a database of 30 min of video (including more than 5000 facial images) and 200 hr of video, respectively. Face tracking is benchmarked using 20 mobile video sequences, which cover a broad range of human activities and environmental changes. We describe the construction of the low bit-rate video coder that uses face detection and tracking to segment foreground/background object and encode them with a different bit-rate. We also describe a framework for smart videoconferences and experimental results on low bit-rate image coding for this application.

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.

Deblocking of block-transform compressed images using weighted sums of symmetrically aligned pixels

A new class of related algorithms for deblocking block-transform compressed images and video sequences is proposed in this paper. The algorithms apply weighted sums on pixel quartets, which are symmetrically aligned with respect to block boundaries. The basic weights, which are aimed at very low bit-rate images, are obtained from a two-dimensional function which obeys predefined constraints. Using these weights on images compressed at higher bit rates produces a deblocked image which contains blurred "false" edges near real edges. We refer to this phenomenon as the ghosting effect. In order to prevent its occurrences, the weights of pixels, which belong to nonmonotone areas, are modified by dividing each pixel's weight by a predefined factor called a grade. This scheme is referred to as weight adaptation by grading (WABG). Better deblocking of monotone areas is achieved by applying three iterations of the WABG scheme on such areas followed by a fourth iteration which is applied on the rest of the image. We refer to this scheme as deblocking frames of variable size (DFOVS). DFOVS automatically adapts itself to the activity of each block. This new class of algorithms produces very good subjective results and PSNR results which are competitive relative to available state-of-the-art methods.

Robust stack-run coding for low bit-rate image transmission over noisy channels

We propose a multiresolution algorithm to jointly optimize a source coder and channel coder. The variable-rate source coder combines an optimal bit-allocation strategy and efficient stack-run coding. This compression scheme, concatenated with appropriate rate-compatible punctured convolutional codes, provides a competitive approach to state-of-the-art extensions of zerotree methods to noisy channels. Results show that under the assumption of almost zero probability of decoding error, the proposed scheme provides good performance for a lower complexity.

An optimal L-filter for reducing blocking artifacts using genetic algorithms

Low bit-rate image coding is essential for many visual communication applications. The block-based discrete cosine transform coding has been widely adopted in image or video compression. However, the coding produces blocking artifacts that severely degrade the perceptual quality of the images or videos. In this paper, an L-filter combining a genetic algorithm (GA) for reducing the blocking artifacts in compressed images is proposed. The L-filter is used for reducing the block artifacts and the GA is used to search the proper parameters for the L-filter. The proposed approach has the advantage of combining the powerful enhancement of the L-filter and the global solution exploration of the GA. In experiments, compressed images are processed by the proposed approach and other methods for comparison. The experimental results reveal that the proposed approach provides a simple but effective way to produce the best result based on the visual inspection and the PSNR objective criterion.