Efficient Encoding of Wireless Capsule Endoscopy Images Using Direct Compression of Colour Filter Array Images

Since its invention in 2001, wireless capsule endoscopy (WCE) has played an important role in the endoscopic examination of the gastrointestinal tract. During this period, WCE has undergone tremendous advances in technology, making it the first-line modality for diseases from bleeding to cancer in the small-bowel. Current research efforts are focused on evolving WCE to include functionality such as drug delivery, biopsy, and active locomotion. For the integration of these functionalities into WCE, two critical prerequisites are the image quality enhancement and the power consumption reduction. An efficient image compression solution is required to retain the highest image quality while reducing the transmission power. The issue is more challenging due to the fact that image sensors in WCE capture images in Bayer Colour filter array (CFA) format. Therefore, standard compression engines provide inferior compression performance. The focus of this thesis is to design an optimized image compression pipeline to encode the capsule endoscopic (CE) image efficiently in CFA format. To this end, this thesis proposes two image compression schemes. First, a lossless image compression algorithm is proposed consisting of an optimum reversible colour transformation, a low complexity prediction model, a corner clipping mechanism and a single context adaptive Golomb-Rice entropy encoder. The derivation of colour transformation that provides the best performance for a given prediction model is considered as an optimization problem. The low complexity prediction model works in raster order fashion and requires no buffer memory. The application of colour transformation yields lower inter-colour correlation and allows the efficient independent encoding of the colour components. The second compression scheme in this thesis is a lossy compression algorithm with a integer discrete cosine transformation at its core. Using the statistics obtained from a large dataset of CE image, an optimum colour transformation is derived using the principal component analysis (PCA). The transformed coefficients are quantized using optimized quantization table, which was designed with a focus to discard medically irrelevant information. A fast demosaicking algorithm is developed to reconstruct the colour image from the lossy CFA image in the decoder. Extensive experiments and comparisons with state-of-the-art lossless image compression methods establish the superiority of the proposed compression methods as simple and efficient image compression algorithm. The lossless algorithm can transmit the image in a lossless manner within the available bandwidth. On the other hand, performance evaluation of lossy compression algorithm indicates that it can deliver high quality images at low transmission power and low computation costs

Image and Video Coding Techniques for Ultra-low Latency

The next generation of wireless networks fosters the adoption of latency-critical applications such as XR, connected industry, or autonomous driving. This survey gathers implementation aspects of different image and video coding schemes and discusses their tradeoffs. Standardized video coding technologies such as HEVC or VVC provide a high compression ratio, but their enormous complexity sets the scene for alternative approaches like still image, mezzanine, or texture compression in scenarios with tight resource or latency constraints. Regardless of the coding scheme, we found inter-device memory transfers and the lack of sub-frame coding as limitations of current full-system and software-programmable implementations.publishedVersionPeer reviewe

Bi-criteria Pipeline Mappings for Parallel Image Processing

Mapping workflow applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline graphs. Several antagonistic criteria should be optimized, such as throughput and latency (or a combination). Typical applications include digital image processing, where images are processed in steady-state mode. In this paper, we study the mapping of a particular image processing application, the JPEG encoding. Mapping pipelined JPEG encoding onto parallel platforms is useful for instance for encoding Motion JPEG images. As the bi-criteria mapping problem is NP-complete, we concentrate on the evaluation and performance of polynomial heuristics

Capsule endoscopy system with novel imaging algorithms

Wireless capsule endoscopy (WCE) is a state-of-the-art technology to receive images of human intestine for medical diagnostics. In WCE, the patient ingests a specially designed electronic capsule which has imaging and wireless transmission capabilities inside it. While the capsule travels through the gastrointestinal (GI) tract, it captures images and sends them wirelessly to an outside data logger unit. The data logger stores the image data and then they are transferred to a personal computer (PC) where the images are reconstructed and displayed for diagnosis. The key design challenge in WCE is to reduce the area and power consumption of the capsule while maintaining acceptable image reconstruction. In this research, the unique properties of WCE images are identified by analyzing hundreds of endoscopic images and video frames, and then these properties are used to develop novel and low complexity compression algorithms tailored for capsule endoscopy. The proposed image compressor consists of a new YEF color space converter, lossless prediction coder, customizable chrominance sub-sampler and an efficient Golomb-Rice encoder. The scheme has both lossy and lossless modes and is further customized to work with two lighting modes – conventional white light imaging (WLI) and emerging narrow band imaging (NBI). The average compression ratio achieved using the proposed lossy compression algorithm is 80.4% for WBI and 79.2% for NBI with high reconstruction quality index for both bands. Two surveys have been conducted which show that the reconstructed images have high acceptability among medical imaging doctors and gastroenterologists. The imaging algorithms have been realized in hardware description language (HDL) and their functionalities have been verified in field programmable gate array (FPGA) board. Later it was implemented in a 0.18 μm complementary metal oxide semiconductor (CMOS) technology and the chip was fabricated. Due to the low complexity of the core compressor, it consumes only 43 µW of power and 0.032 mm2 of area. The compressor is designed to work with commercial low-power image sensor that outputs image pixels in raster scan fashion, eliminating the need of significant input buffer memory. To demonstrate the advantage, a prototype of the complete WCE system including an FPGA based electronic capsule, a microcontroller based data logger unit and a Windows based image reconstruction software have been developed. The capsule contains the proposed low complexity image compressor and can generate both lossy and lossless compressed bit-stream. The capsule prototype also supports both white light imaging (WLI) and narrow band imaging (NBI) imaging modes and communicates with the data logger in full duplex fashion, which enables configuring the image size and imaging mode in real time during the examination. The developed data logger is portable and has a high data rate wireless connectivity including Bluetooth, graphical display for real time image viewing with state-of-the-art touch screen technology. The data are logged in micro SD cards and can be transferred to PC or Smartphone using card reader, USB interface, or Bluetooth wireless link. The workstation software can decompress and show the reconstructed images. The images can be navigated, marked, zoomed and can be played as video. Finally, ex-vivo testing of the WCE system has been done in pig's intestine to validate its performance

LOCO-ANS: An Optimization of JPEG-LS Using an Efficient and Low-Complexity Coder Based on ANS

Near-lossless compression is a generalization of lossless compression, where the codec user is able to set the maximum absolute difference (the error tolerance) between the values of an original pixel and the decoded one. This enables higher compression ratios, while still allowing the control of the bounds of the quantization errors in the space domain. This feature makes them attractive for applications where a high degree of certainty is required. The JPEG-LS lossless and near-lossless image compression standard combines a good compression ratio with a low computational complexity, which makes it very suitable for scenarios with strong restrictions, common in embedded systems. However, our analysis shows great coding efficiency improvement potential, especially for lower entropy distributions, more common in near-lossless. In this work, we propose enhancements to the JPEG-LS standard, aimed at improving its coding efficiency at a low computational overhead, particularly for hardware implementations. The main contribution is a low complexity and efficient coder, based on Tabled Asymmetric Numeral Systems (tANS), well suited for a wide range of entropy sources and with simple hardware implementation. This coder enables further optimizations, resulting in great compression ratio improvements. When targeting photographic images, the proposed system is capable of achieving, in mean, 1.6%, 6%, and 37.6% better compression for error tolerances of 0, 1, and 10, respectively. Additional improvements are achieved increasing the context size and image tiling, obtaining 2.3% lower bpp for lossless compression. Our results also show that our proposal compares favorably against state-of-the-art codecs like JPEG-XL and WebP, particularly in near-lossless, where it achieves higher compression ratios with a faster coding speedThis work was supported in part by the Spanish Research Agency through the Project AgileMon under Grant AEI PID2019-104451RB-C2

Joint Optimization of Low-power DCT Architecture and Effcient Quantization Technique for Embedded Image Compression

International audienceThe Discrete Cosine Transform (DCT)-based image com- pression is widely used in today's communication systems. Signi cant research devoted to this domain has demonstrated that the optical com- pression methods can o er a higher speed but su er from bad image quality and a growing complexity. To meet the challenges of higher im- age quality and high speed processing, in this chapter, we present a joint system for DCT-based image compression by combining a VLSI archi- tecture of the DCT algorithm and an e cient quantization technique. Our approach is, rstly, based on a new granularity method in order to take advantage of the adjacent pixel correlation of the input blocks and to improve the visual quality of the reconstructed image. Second, a new architecture based on the Canonical Signed Digit and a novel Common Subexpression Elimination technique is proposed to replace the constant multipliers. Finally, a recon gurable quantization method is presented to e ectively save the computational complexity. Experimental results obtained with a prototype based on FPGA implementation and com- parisons with existing works corroborate the validity of the proposed optimizations in terms of power reduction, speed increase, silicon area saving and PSNR improvement

A low complexity image compression algorithm for Bayer color filter array

Digital image in their raw form requires an excessive amount of storage capacity. Image compression is a process of reducing the cost of storage and transmission of image data. The compression algorithm reduces the file size so that it requires less storage or transmission bandwidth. This work presents a new color transformation and compression algorithm for the Bayer color filter array (CFA) images. In a full color image, each pixel contains R, G, and B components. A CFA image contains single channel information in each pixel position, demosaicking is required to construct a full color image. For each pixel, demosaicking constructs the missing two-color information by using information from neighbouring pixels. After demosaicking, each pixel contains R, G, and B information, and a full color image is constructed. Conventional CFA compression occurs after the demosaicking. However, the Bayer CFA image can be compressed before demosaicking which is called compression-first method, and the algorithm proposed in this research follows the compression-first or direct compression method. The compression-first method applies the compression algorithm directly onto the CFA data and shifts demosaicking to the other end of the transmission and storage process. The advantage of the compression-first method is that it requires three time less transmission bandwidth for each pixel than conventional compression. Compression-first method of CFA data produces spatial redundancy, artifacts, and false high frequencies. The process requires a color transformation with less correlation among the color components than that Bayer RGB color space. This work analyzes correlation coefficient, standard deviation, entropy, and intensity range of the Bayer RGB color components. The analysis provides two efficient color transformations in terms of features of color transformation. The proposed color components show lesser correlation coefficient than occurs with the Bayer RGB color components. Color transformations reduce both the spatial and spectral redundancies of the Bayer CFA image. After color transformation, the components are independently encoded using differential pulse-code modulation (DPCM) in raster order fashion. The residue error of DPCM is mapped to a positive integer for the adaptive Golomb rice code. The compression algorithm includes both the adaptive Golomb rice and Unary coding to generate bit stream. Extensive simulation analysis is performed on both simulated CFA and real CFA datasets. This analysis is extended for the WCE (wireless capsule endoscopic) images. The compression algorithm is also realized with a simulated WCE CFA dataset. The results show that the proposed algorithm requires less bits per pixel than the conventional CFA compression. The algorithm also outperforms recent works on CFA compression algorithms for both real and simulated CFA datasets

Compressive sensing based image processing and energy-efficient hardware implementation with application to MRI and JPG 2000

In the present age of technology, the buzzwords are low-power, energy-efficient and compact systems. This directly leads to the date processing and hardware techniques employed in the core of these devices. One of the most power-hungry and space-consuming schemes is that of image/video processing, due to its high quality requirements. In current design methodologies, a point has nearly been reached in which physical and physiological effects limit the ability to just encode data faster. These limits have led to research into methods to reduce the amount of acquired data without degrading image quality and increasing the energy consumption. Compressive sensing (CS) has emerged as an efficient signal compression and recovery technique, which can be used to efficiently reduce the data acquisition and processing. It exploits the sparsity of a signal in a transform domain to perform sampling and stable recovery. This is an alternative paradigm to conventional data processing and is robust in nature. Unlike the conventional methods, CS provides an information capturing paradigm with both sampling and compression. It permits signals to be sampled below the Nyquist rate, and still allowing optimal reconstruction of the signal. The required measurements are far less than those of conventional methods, and the process is non-adaptive, making the sampling process faster and universal. In this thesis, CS methods are applied to magnetic resonance imaging (MRI) and JPEG 2000, which are popularly used imaging techniques in clinical applications and image compression, respectively. Over the years, MRI has improved dramatically in both imaging quality and speed. This has further revolutionized the field of diagnostic medicine. However, imaging speed, which is essential to many MRI applications still remains a major challenge. The specific challenge addressed in this work is the use of non-Fourier based complex measurement-based data acquisition. This method provides the possibility of reconstructing high quality MRI data with minimal measurements, due to the high incoherence between the two chosen matrices. Similarly, JPEG2000, though providing a high compression, can be further improved upon by using compressive sampling. In addition, the image quality is also improved. Moreover, having a optimized JPEG 2000 architecture reduces the overall processing, and a faster computation when combined with CS. Considering the requirements, this thesis is presented in two parts. In the first part: (1) A complex Hadamard matrix (CHM) based 2D and 3D MRI data acquisition with recovery using a greedy algorithm is proposed. The CHM measurement matrix is shown to satisfy the necessary condition for CS, known as restricted isometry property (RIP). The sparse recovery is done using compressive sampling matching pursuit (CoSaMP); (2) An optimized matrix and modified CoSaMP is presented, which enhances the MRI performance when compared with the conventional sampling; (3) An energy-efficient, cost-efficient hardware design based on field programmable gate array (FPGA) is proposed, to provide a platform for low-cost MRI processing hardware. At every stage, the design is proven to be superior with other commonly used MRI-CS methods and is comparable with the conventional MRI sampling. In the second part, CS techniques are applied to image processing and is combined with JPEG 2000 coder. While CS can reduce the encoding time, the effect on the overall JPEG 2000 encoder is not very significant due to some complex JPEG 2000 algorithms. One problem encountered is the big-level operations in JPEG 2000 arithmetic encoding (AE), which is completely based on bit-level operations. In this work, this problem is tackled by proposing a two-symbol AE with an efficient FPGA based hardware design. Furthermore, this design is energy-efficient, fast and has lower complexity when compared to conventional JPEG 2000 encoding

Development of Low Power Image Compression Techniques

Digital camera is the main medium for digital photography. The basic operation performed by a simple digital camera is, to convert the light energy to electrical energy, then the energy is converted to digital format and a compression algorithm is used to reduce memory requirement for storing the image. This compression algorithm is frequently called for capturing and storing the images. This leads us to develop an efficient compression algorithm which will give the same result as that of the existing algorithms with low power consumption. As a result the new algorithm implemented camera can be used for capturing more images then the previous one. 1) Discrete Cosine Transform (DCT) based JPEG is an accepted standard for lossy compression of still image. Quantisation is mainly responsible for the amount loss in the image quality in the process of lossy compression. A new Energy Quantisation (EQ) method proposed for speeding up the coding and decoding procedure while preserving image qu..