Showing papers on "Entropy encoding published in 2016"

Constant Composition Distribution Matching

Abstract: Distribution matching transforms independent and Bernoulli(1/2) distributed input bits into a sequence of output symbols with a desired distribution. Fixed-to-fixed length, invertible, and low complexity encoders and decoders based on constant composition and arithmetic coding are presented. The encoder achieves the maximum rate, namely, the entropy of the desired distribution, asymptotically in the blocklength. Furthermore, the normalized divergence of the encoder output and the desired distribution goes to zero in the blocklength.

Full Resolution Image Compression with Recurrent Neural Networks

Abstract: This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural network for entropy coding. We compare RNN types (LSTM, associative LSTM) and introduce a new hybrid of GRU and ResNet. We also study "one-shot" versus additive reconstruction architectures and introduce a new scaled-additive framework. We compare to previous work, showing improvements of 4.3%-8.8% AUC (area under the rate-distortion curve), depending on the perceptual metric used. As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.

Lossless Compression of JPEG Coded Photo Collections

Abstract: The explosion of digital photos has posed a significant challenge to photo storage and transmission for both personal devices and cloud platforms. In this paper, we propose a novel lossless compression method to further reduce the size of a set of JPEG coded correlated images without any loss of information. The proposed method jointly removes inter/intra image redundancy in the feature, spatial, and frequency domains. For each collection, we first organize the images into a pseudo video by minimizing the global prediction cost in the feature domain. We then present a hybrid disparity compensation method to better exploit both the global and local correlations among the images in the spatial domain. Furthermore, the redundancy between each compensated signal and the corresponding target image is adaptively reduced in the frequency domain. Experimental results demonstrate the effectiveness of the proposed lossless compression method. Compared with the JPEG coded image collections, our method achieves average bit savings of more than 31%.

Constant SNR, Rate Control, and Entropy Coding for Predictive Lossy Hyperspectral Image Compression

Abstract: Predictive lossy compression has been shown to represent a very flexible framework for lossless and lossy onboard compression of multispectral and hyperspectral images with quality and rate control. In this paper, we improve predictive lossy compression in several ways, using a standard issued by the Consultative Committee on Space Data Systems, namely CCSDS-123, as an example of application. First, exploiting the flexibility in the error control process, we propose a constant-signal-to-noise-ratio algorithm that bounds the maximum relative error between each pixel of the reconstructed image and the corresponding pixel of the original image. This is very useful to avoid low-energy areas of the image being affected by large errors. Second, we propose a new rate control algorithm that has very low complexity and provides performance equal to or better than existing work. Third, we investigate several entropy coding schemes that can speed up the hardware implementation of the algorithm and, at the same time, improve coding efficiency. These advances make predictive lossy compression an extremely appealing framework for onboard systems due to its simplicity, flexibility, and coding efficiency.

Improved entropy encoding for high efficient video coding standard

Abstract: The High Efficiency Video Coding (HEVC) has better coding efficiency, but the encoding performance has to be improved to meet the growing multimedia applications. This paper improves the standard entropy encoding by introducing the optimized weighing parameters, so that higher rate of compression can be accomplished over the standard entropy encoding. The optimization is performed using the recently introduced firefly algorithm. The experimentation is carried out using eight benchmark video sequences and the PSNR for varying rate of data transmission is investigated. Comparative analysis based on the performance statistics is made with the standard entropy encoding. From the obtained results, it is clear that the originality of the decoded video sequence is preserved far better than the proposed method, though the compression rate is increased.

Region-adaptive hierarchical transform and entropy coding for point cloud compression, and corresponding decompression

Abstract: Innovations in compression and decompression of point cloud data are described. For example, an encoder is configured to encode point cloud data, thereby producing encoded data. In particular, the encoder applies a region-adaptive hierarchical transform (“RAHT”) to attributes of occupied points, thereby producing transform coefficients. The encoder can also quantize the transform coefficients and perform adaptive entropy coding of the quantized transform coefficients. For corresponding decoding, a decoder is configured to decode the encoded data to reconstruct point cloud data. In particular, the decoder applies an inverse RAHT to transform coefficients for attributes of occupied points. The decoder can also perform adaptive entropy decoding and inverse quantization of the quantized transform coefficients. The adaptive entropy coding/decoding can use estimates of the distribution of values for the quantized transform coefficients. In this case, the encoder calculates the estimates and signals them to the decoder.

VLSI Implementation of a Cost-Efficient Near-Lossless CFA Image Compressor for Wireless Capsule Endoscopy

Abstract: In this paper, a novel near-lossless color filter array (CFA) image compression algorithm based on JPEG-LS is proposed for VLSI implementation. It consists of a pixel restoration, a prediction, a run mode, and entropy coding modules. According to the information of the previous research, a context table and row memory consumed more than 81% hardware cost in a JPEG-LS encoder design. Hence, in this paper, a novel context-free and near-lossless image compression algorithm is presented. Since removing the context model causes decreasing of the compression performance, a novel prediction, run mode, and modified Golomb-Rice coding techniques were used to improve the compression efficiency. The VLSI architecture of the proposed image compressor consists of a register bank, a pixel restoration module, a predictor, a run mode module, and an entropy encoder. A pipeline technique was used to improve the performance of this. It contains only 10.9k gate count, and the core area is 30625 μm 2 , synthesized by using a 90-nm CMOS process. Compared with the previous JPEG-LS designs, this paper reduces the gate counts by 44.1% and 41.7%, respectively, for five standard and eight endoscopy testing images in CFA format. It also improves the average PSNR values by 0.96 and 0.43 dB, respectively, for the same test images.

Lossless Frame Memory Compression Using Pixel-Grain Prediction and Dynamic Order Entropy Coding

Abstract: Power constraints constitute a critical design issue for the portable video codec system, in which the external dynamic random access memory (DRAM) accounts for more than half of the overall system power requirements. With the ultrahigh-definition video specifications, the power consumed by accessing reference frames in the external DRAM has become the bottleneck for the portable video encoding system design. To relieve the dynamic power stresses introduced by the DRAM, a lossless compression algorithm is devised to reduce the external traffic and the memory requirements of reference frames. First, pixel-granularity directional prediction is adopted to decrease the prediction residual energy by 54.1% over the previous horizontal prediction. Second, the dynamic $k$ th-order unary/Exp-Golomb rice coding is applied to accommodate the large-valued prediction residues. With the aforementioned techniques, an average data traffic reduction of 68.5% for the off-chip reference frames is obtained, which consequently reduces the dynamic power requirements of the DRAM by 42.3%. Based on the high data reduction ratio of the proposed compression algorithm, a partition group table-based storage space reduction scheme is provided to improve the utilization of row buffers in the DRAM. Consequently, an additional 14.5% of the DRAM dynamic power can be saved by reducing the number of row buffer activations. In total, a 56.8% decrease in the dynamic power requirements of the external reference frame access can be obtained using our strategies. With TSMC 65-nm CMOS logic technology, our algorithm was implemented in a parallel VLSI architecture based on a compressor and decompressor at the cost of 36.5k and 34.7k, respectively, in terms of gate count. The throughputs of the proposed compressor and decompressor are 1.54 and 0.78 Gpixels/s, which are suitable for quad full high definition (4K) @ 94 frames/s real-time encoding with the level-D reference data reuse scheme.

A Non-format Compliant Scalable RSA-based JPEG Encryption Algorithm

Abstract: A non-format compliant JPEG encryption algorithm is proposed which is based on a modification of the RSA encryption system. Firstly, an alternate form of entropy coding is described, which is more suited to the proposed algorithm, instead of the zigzag coding scheme used in JPEG. The algorithm for the encryption and decryption process is then elaborated. A variant of the algorithm, also based on the RSA algorithm is also described, which is faster than the original algorithm, but expands the bit stream slightly. Both the algorithms are shown to be scalable and resistant to ‘sketch’ attacks. Finally, the encrypted file sizes for both the algorithms are compared with the unencrypted JPEG compressed image file size. The encrypted image is found to be moderately expanded, but which is justified by the high security and most importantly, the scalability of the algorithm.

(R, S)-Norm Information Measure and A Relation Between Coding and Questionnaire Theory

Abstract: In this paper, we introduce a quantity which is called (R, S)-norm entropy and discuss some of its major properties in comparison with Shannon’s and other entropies known in the literature. Further, we give an application of (R, S)-norm entropy in coding theory and a coding theorem analogous to the ordinary coding theorem for a noiseless channel. The theorem states that the proposed entropy is the lower bound of mean code word length. Further, we give an application of (R, S)-norm entropy and noiseless coding theorem in questionnaire theory. We show that the relationship between noiseless coding theorem and questionnaire theory through a charging scheme based on the resolution of questions and lower bound on the measure of the charge can also be obtained.

Method of coding and decoding images, coding and decoding device and computer programs corresponding thereto

Abstract: A method for coding includes; segmenting an image into blocks; grouping blocks into a number of subsets; coding, using an entropy coding module, each subset, by associating digital information with symbols of each block of a subset, including, for the first block of the image, initializing state variables of the coding module; and generating a data sub-stream representative of at least one of the coded subsets of blocks. Where a current block is the first block to be coded of a subset, symbol occurrence probabilities for the first current block are determined based on those for a coded and decoded predetermined block of at least one other subset. Where the current block is the last coded block of the subset: writing, in the sub-stream representative of the subset, the entire the digital information associated with the symbols during coding of the blocks of the subset, and implementing the initializing sub-step.

Advanced Video Coding Systems

Abstract: This book presents an overview of the state of the art in video coding technology. Specifically, it introduces the tools of the AVS2 standard, describing how AVS2 can help to achieve a significant improvement in coding efficiency for future video networks and applications by incorporating smarter coding tools such as scene video coding. Features: introduces the basic concepts in video coding, and presents a short history of video coding technology and standards; reviews the coding framework, main coding tools, and syntax structure of AVS2; describes the key technologies used in the AVS2 standard, including prediction coding, transform coding, entropy coding, and loop-filters; examines efficient tools for scene video coding and surveillance video, and the details of a promising intelligent video coding system; discusses optimization technologies in video coding systems; provides a review of image, video, and 3D content quality assessment algorithms; surveys the hot research topics in video compression.

Spectral Shape Estimation in Data Compression for Smart Grid Monitoring

Abstract: This paper proposes a transform-based compression algorithm for waveforms associated with power quality and transient phenomena in power systems. This method uses the wavelet transform, a dynamic bit allocation in the transform domain through estimation of the spectral shape, as well as entropy coding in order to minimize residual redundancy. Five distinct approaches for estimating the spectral shape are proposed. Four of them are based on analytical models that seek to describe the decreasing behavior of the transformed coefficients: 1) decreasing linear bit allocation shape; 2) decreasing quadratic bit allocation shape; 3) decreasing exponential bit allocation shape; 4) rotated sigmoid bit allocation shape; and 5) the fifth approach—the neural shape estimator (NSE)—is an adaptive model that uses an artificial neural network to map the changes in the spectrum shape. Results with databases of real signals and a performance evaluation using objective measures are reported. The results indicate that the NSE approach outperforms the other proposed solutions that use spectral shape estimation for coding, as well as other compression contributions reported in the literature.

A dynamic motion vector referencing scheme for video coding

Abstract: Video codecs exploit temporal redundancy in video signals, through the use of motion compensated prediction, to achieve superior compression performance. The coding of motion vectors takes a large portion of the total rate cost. Prior research utilizes the spatial and temporal correlation of the motion field to improve the coding efficiency of the motion information. It typically constructs a candidate pool composed of a fixed number of reference motion vectors and allows the codec to select and reuse the one that best approximates the motion of the current block. This largely disconnects the entropy coding process from the block's motion information, and throws out any information related to motion consistency, leading to sub-optimal coding performance. An alternative motion vector referencing scheme is proposed in this work to fully accommodate the dynamic nature of the motion field. It adaptively extends or shortens the candidate list according to the actual number of available reference motion vectors. The associated probability model accounts for the likelihood that an individual motion vector candidate is used. A complementary motion vector candidate ranking system is also presented here. It is experimentally shown that the proposed scheme achieves about 1.6% compression performance gains on a wide range of test clips.

Advanced arithmetic coder

Abstract: An example method of entropy coding video data includes obtaining a pre-defined initialization value for a context of a plurality of contexts used in a context-adaptive entropy coding process to entropy code a value for a syntax element in a slice of the video data, wherein the pre-defined initialization value is stored with N-bit precision; determining, using a look-up table and based on the pre-defined initialization value, an initial probability state of the context for the slice of the video data, wherein a number of possible probability states for the context is greater than two raised to the power of N; and entropy coding, based on the initial probability state of the context, a bin of the value for the syntax element.

A CU-Level Rate and Distortion Estimation Scheme for RDO of Hardware-Friendly HEVC Encoders Using Low-Complexity Integer DCTs

Abstract: In this paper, a low complexity coding unit (CU)-level rate and distortion estimation scheme is proposed for High Efficiency Video Coding (HEVC) hardware-friendly implementation where a Walsh–Hadamard transform (WHT)-based low-complexity integer discrete cosine transform (DCT) is employed for distortion estimation. Since HEVC adopts quadtree structures of coding blocks with hierarchical coding depths, it becomes more difficult to estimate accurate rate and distortion values without actually performing transform, quantization, inverse transform, de-quantization, and entropy coding. Furthermore, DCT for rate-distortion optimization (RDO) is computationally high, because it requires a number of multiplication and addition operations for various transform block sizes of 4-, 8-, 16-, and 32-orders and requires recursive computations to decide the optimal depths of CU or transform unit. Therefore, full RDO-based encoding is highly complex, especially for low-power implementation of HEVC encoders. In this paper, a rate and distortion estimation scheme is proposed in CU levels based on a low-complexity integer DCT that can be computed in terms of WHT whose coefficients are produced in prediction stages. For rate and distortion estimation in CU levels, two orthogonal matrices of $4\times 4$ and $8\times 8$ , which are applied to WHT that are newly designed in a butterfly structure only with addition and shift operations. By applying the integer DCT based on the WHT and newly designed transforms in each CU block, the texture rate can precisely be estimated after quantization using the number of non-zero quantized coefficients and the distortion can also be precisely estimated in transform domain without de-quantization and inverse transform required. In addition, a non-texture rate estimation is proposed by using a pseudoentropy code to obtain accurate total rate estimates. The proposed rate and the distortion estimation scheme can effectively be used for HW-friendly implementation of HEVC encoders with 9.8% loss over HEVC full RDO, which much less than 20.3% and 30.2% loss of a conventional approach and Hadamard-only scheme, respectively.

Nested Entropy Encoding

Abstract: Methods and systems for improving coding decoding efficiency of video by providing a syntax modeler, a buffer, and a decoder. The syntax modeler may associate a first sequence of symbols with syntax elements. The buffer may store tables, each represented by a symbol in the first sequence, and each used to associate a respective symbol in a second sequence of symbols with encoded data. The decoder decodes the data into a bitstream using the second sequence retrieved from a table.

Motion picture encoding apparatus and method thereof

Abstract: Provided is a video encoding apparatus, including a signal separator to separate a differential image block into a first domain and a second domain, based on a boundary line included in the differential image block, the differential image block indicating a difference between an original image and a prediction image with respect to the original image, a transform encoder to perform a transform encoding with respect to the first domain using a discrete cosine transform (DCT), a quantization unit to quantize an output of the transform encoding unit in a frequency domain, a space domain quantization unit to quantize the second domain in a space domain, and an entropy encoder to perform an entropy encoding using outputs of the quantization unit and the space domain quantization unit.

Improved rate distortion optimized quantization for HEVC with adaptive thresholding

Abstract: Coefficient-level rate distortion optimized quantization (RDOQ) is an efficient tool to improve rate-distortion performance with 6%–8% bit-rate saving. It has been widely adopted in video encoders such as JM, x264, HM and so on. However, software implementation of RDOQ suffers from high computation complexity due to intensive path search and from data dependency caused by context based entropy coding. In this paper, a fast RDOQ algorithm with accurate rate estimation between the two candidates quantized level for high efficient video coding (HEVC) is proposed to decrease the computation complexity and break the data dependency. Firstly, the preselection of candidate quantized levels is preformed according to hard decision quantization (HDQ) level and the coefficient position in transform unit (TU). The reduction of candidate quantized levels can significant decrease the computation complexity of RDOQ. Secondly, a model of delta bit-rate is constructed to estimate bitrate change between the rest two candidate quantized levels. With this delta bit-rate model, the high computation complexity of bitrate computation based on context adaptive binary arithmetic coding (CABAC) can be avoided, and coefficients can be parallel processed. Experiment results demonstrate that the proposed algorithm can decrease the encoding time of RDOQ by 54.36% in average with no more than 0.88% BD-rate loss.

Method and apparatus of entropy coding and context modelling for video and image coding

Abstract: A method and apparatus for applying entropy coding to a symbol associated with a block are disclosed According to the present invention, context-based entropy coding is applied to source symbols associated with blocks having variable block sizes generated by partitioning an initial block using a quadtree structure, a binary-tree structure or a combined quadtree plus binary-tree structure Contexts according to the present invention are based on some information derived from neighbouring blocks and also based on at least one of the shape, the size and the depth of the current block since the statistics of the symbols associated with the current block may be correlated with how the current block has been partitioned through a tree structure A current symbol to be encoded or decoded may correspond to split flags and modes associated with the tree structure, skip flag or prediction mode flag

K-D tree encoding for point clouds using deviations

Abstract: An encoder includes a processor, a buffer, and a memory. The memory includes code as instructions that cause the processor to perform a number of steps. The steps include quantizing geometric data associated with a geometric construct, partitioning the geometric construct, determining a number of points in the partition, generating a deviation value based on the number of points in the partition, storing the deviation value in the buffer, and entropy encoding the deviation value.

Almost lossless variable-length source coding on countably infinite alphabets

Abstract: Motivated from the fact that universal source coding on countably infinite alphabets is not feasible, the notion of almost lossless source coding is introduced. This idea -analog to the weak variable-length source coding problem proposed by Han [1]- aims at relaxing the lossless block-wise assumption to allow a distortion that vanishes asymptotically as the block-length goes to infinity1. In this setup, both feasibility and optimality results are derived for the case of memoryless sources defined on countably infinite alphabets. Our results show on one hand that Shannon entropy characterizes the minimum achievable rate (known statistics) while on the other that almost lossless universal source coding becomes feasible for the family of finite entropy stationary and memoryless sources with countably infinite alphabets.

Lossless depth map coding using binary tree based decomposition and context-based arithmetic coding

Abstract: Depth maps are becoming increasingly important in the context of emerging video coding and processing applications. Depth images represent the scene surface and are characterized by areas of smoothly varying grey levels separated by sharp edges at the position of object boundaries. To enable high quality view rendering at the receiver side, preservation of these characteristics is important. Lossless coding enables avoiding rendering artifacts in synthesized views due to depth compression artifacts. In this paper, we propose a binary tree based lossless depth coding scheme that arranges the residual frame into integer or binary residual bitmap. High spatial correlation in depth residual frame is exploited by creating large homogeneous blocks of adaptive size, which are then coded as a unit using context based arithmetic coding. On the standard 3D video sequences, the proposed lossless depth coding has achieved compression ratio in the range of 20 to 80.

Hiding depth information in compressed 2D image/video using reversible watermarking

Abstract: In this paper, a novel joint coding scheme is proposed for 3D media content including stereo images and multiview-plus-depth (MVD) video for the purpose of depth information hiding. The depth information is an image or image channel which reveals the distance of scene objects' surfaces from a viewpoint. With the concern of copyright protection, access control and coding efficiency for 3D content, we propose to hide the depth information into the texture image/video by a reversible watermarking algorithm called Quantized DCT Expansion (QDCTE). Considering the crucial importance of depth information for depth-image-based rendering (DIBR), full resolution depth image/video is compressed and embedded into the texture image/video, and it can be extracted without extra quality degradation other than compression itself. The reversibility of the proposed algorithm guarantees that texture image/video quality will not suffer from the watermarking process even if high payload (i.e. depth information) is embedded into the cover image/video. In order to control the size increase of watermarked image/video, the embedding function is carefully selected and the entropy coding process is also customized according to watermarking strength. Huffman and content-adaptive variable-length coding (CAVLC), which are respectively used for JPEG image and H.264 video entropy encoding, are analyzed and customized. After depth information embedding, we propose a new method to update the entropy codeword table with high efficiency and low computational complexity according to watermark embedding strength. By using our proposed coding scheme, the depth information can be hidden into the compressed texture image/video with little bitstream size overhead while the quality degradation of original cover image/video from watermarking can be completely removed at the receiver side.

Digital medical image compression algorithm using adaptive Huffman coding and graph based quantization based on IWT-SVD

Abstract: Currently, the data storage medium in digital form are widely applied in various fields, including in the medical world. The excessive size of the digital medical image digital poses problems in terms of storage and the time to transmit these images through the Internet. To overcome these problems, a digital image compression can be conducted. This procedure is conducted to maintain the image quality therefore it does not affect the outcome of a medical image diagnosis. In [1], it has carried out and examined the implementation of the wavelet-based image compression SVD, where the quantity process using graph coloring and for entropy encoding using Arithmetic Coding. In this paper, the data used is a medical image. Furthermore, it has been designed an image compression system based on Integer Wavelet Transform (IWT) and SVD. Graph-based quantization used still refers to [1], but entropy encoding used is adaptive Huffman coding. In addition, IWT used in this system because it is reversible. Performance compression system designed to produce an average PSNR value in the range of 50–53 dB and the compression ratio ranges from 78–85%.

Entropy Coding of Spectral Envelopes for Speech and Audio Coding Using Distribution Quantization.

Abstract: Speech and audio codecs model the overall shape of the signal spectrum using envelope models. In speech coding the predominant approach is linear predictive coding, which offers high coding efficiency at the cost of computational complexity and a rigid systems design. Audio codecs are usually based on scale factor bands, whose calculation and coding is simple, but whose coding efficiency is lower than that of linear prediction. In the current work we propose an entropy coding approach for scale factor bands, with the objective of reaching the same coding efficiency as linear prediction, but simultaneously retaining a low computational complexity. The proposed method is based on quantizing the distribution of spectral mass using betadistributions. Our experiments show that the perceptual quality achieved with the proposed method is similar to that of linear predictive models with the same bit rate, while the design simultaneously allows variable bit-rate coding and can easily be scaled to different sampling rates. The algorithmic complexity of the proposed method is less than one third of traditional multi-stage vector quantization of linear predictive envelopes.