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Smooth side-match and smooth overlap-match vector quantizers for image coding

OPTICAL ENGINEERING 2000年第7期39卷 1869-1876页

作者： Yang, SB Tseng, LY Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan

The side-match Vector quantizer (SMVQ) is a well known method for image coding. We propose the smooth side-match vector quantizer (SSMVQ). The SSMVQ improves coding quality by making smoother the transition of the gray levels of the adjacent blocks. Moreover, the smooth overlap-match vector quantizer (SOMVQ) is proposed to improve the performance of the overlap-match vector quantizer (OMVQ). Experimental results reveal that SSMVQ and SOMVQ have the higher peak SNR (PSNR) values, better visual perception quality, and lower bit rate than SMVQ and overlap-match vector quantizer (OMVQ), respectively. (C) 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)03407-3].

关键词： smooth side-match vector quantizer smooth overlap-match vector quantizer image coding

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GRAY-SCALE image-coding FOR FREEZE-FRAME VIDE-OCONFERENCING

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IEEE TRANSACTIONS ON COMMUNICATIONS 1986年第4期34卷 382-394页

作者： ANASTASSIOU, D PENNEBAKER, WB MITCHELL, JL IBM CORP THOMAS J WATSON RES CTRYORKTOWN HTSNY 10598

A new gray-scale image coding technique has been developed, in which an extended DPCM approach has been combined with entropy coding. This technique has been implemented in a freeze-frame videoconferencing system which is now operational at IBM sites throughout the world. Following image preprocessing, the two fields of the interlaced 512 x 480 pixel video frame are compressed sequentially with different algorithms. The reconstructed image quality is improved by subsequent image postprocessing, the final reconstructed image being almost indistinguishable from the original image. Typical gray-scale video images compress to about a half bit per pixel and transmit over 4.8 kbit/s dial-up telephone lines in about a half minute. The gray-scale image processing and compression algorithms are described in this paper.

关键词： Gray-scale image coding Teleconferencing Pixel Video compression image reconstruction Entropy coding image quality Telephony image processing

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Improvement of JPEG XL Lossy image coding Using Region Adaptive DCT Block Partitioning Structure

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IEEE ACCESS 2021年 9卷 113213-113225页

作者： Cho, Joonhyung Kwon, Oh-Jin Choi, Seungcheol Sejong Univ Dept Elect Engn Seoul 05006 South Korea

The Joint Picture Expert Group (JPEG) committee has been standardizing next-generation image compression, called JPEG XL, to meet the specific needs for a responsive web, wide color gamut, and high dynamic range. JPEG XL supports lossy and lossless compression. A variable-sized discrete cosine transform (DCT) block is used for lossy compression. A block partitioning method is regarded as a critical function for the performance of JPEG XL. The current DCT block partitioning method used in JPEG XL is highly dependent on the compression rate and tends to assign small-sized DCT blocks to homogeneously textured regions (HTRs) having similar or regular patterns. We propose a region-adaptive DCT block partitioning method that assigns larger blocks to the HTR. The proposed method identifies the HTRs by using a combined metric employing a sum-modified Laplacian, zero-crossing, and colorfulness metric for measuring the region homogeneity. Objective, subjective, and visual comparison evaluations with the ten images recommended by the JPEG working group were provided to show the improvement in coding performance. The proposed method shows its superiority in terms of the compression efficiency evaluated using six objective metrics, subjective tests with 15 participants, visual comparison improvements in the HTR, and gains in the execution time.

关键词： Transform coding image coding Discrete cosine transforms image color analysis Standards Encoding Measurement JPEG XL DCT block partitioning structure homogeneous region sum-modified Laplacian zero-crossing colorfulness

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Semantic-Aware Visual Decomposition for image coding

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2023年第9期131卷 2333-2355页

作者： Chang, Jianhui Zhang, Jian Li, Jiguo Wang, Shiqi Mao, Qi Jia, Chuanmin Ma, Siwei Gao, Wen Peking Univ Natl Engn Res Ctr Visual Technol Sch Comp Sci Beijing 100871 Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen 518055 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China

In this paper, we propose a novel image coding framework with semantic-aware visual decomposition towards extremely low bitrate compression. In particular, an input image is analyzed into a semantic map as structural representation and semantic-wise texture representation and further compressed into bitstreams at the encoder side. On the decoder side, the received bitstreams of dual-layer representations are decoded and reconstructed for target image synthesis with generative models. Moreover, the attention mechanism is introduced into the model architecture for texture representation modeling and a coherency regularization is proposed to further optimize the texture representation space by aligning the representation space with the source pixel space for higher synthesis quality. Besides, we also propose a cross-channel entropy module and control the quantization scale to facilitate rate-distortion optimization. Upon compressing the decomposed components into the bitstream, the simple yet effective representation philosophy benefits image compression in many aspects. First, in terms of compression performance, compact representations, and high visual synthesis quality can bring remarkable advantages. Second, the proposed framework yields a physically explainable bitstream composed of the structural segment and semantic-wise texture segments. Third and most importantly, subsequent vision tasks (e.g., content manipulation) can receive fundamental support from the semantic-aware visual decomposition and synthesis mechanism. Extensive experimental results demonstrate the superiority of the proposed framework towards efficient visual representation learning, high efficiency image compression (< 0.1 bpp), and intelligent visual applications (e.g., manipulation and analysis).

关键词： image coding Semantic-aware visual decomposition Structure-texture Coherency regularization Extremely low bitrate

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INFORMATION LOSS RECOVERY FOR BLOCK-BASED image-coding TECHNIQUES - A FUZZY-LOGIC APPROACH

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IEEE TRANSACTIONS ON image PROCESSING 1995年第3期4卷 259-273页

作者： LEE, XB ZHANG, YQ LEONGARCIA, A UNIV TORONTO DEPT ELECT ENGNVIDEO BISN LABTORONTOONCANADA DAVID SARNOFF RES CTR PRINCETONNJ

A new technique to recover the information loss in a block-based image coding system is developed in this paper, The proposed scheme is based on fuzzy logic reasoning and can be divided into three main steps: 1) hierarchical compass interpolation/extrapolation (HCIE) in the spatial domain for initial recovery of lost blocks that mainly contain low-frequency information such as smooth background 2) coarse spectra interpretation by fuzzy logic reasoning for recovery of lost blocks that contain high-frequency information such as complex textures and fine features 3) sliding window iteration (SWI), which is performed in both spatial and spectral domains to efficiently integrate the results obtained in steps 1) and 2) such that the optimal result can be achieved in terms of surface continuity on block boundaries and a set of fuzzy inference rules. The proposed method, which is suitable for recovering both isolated and contiguous block losses, provides a new approach for error concealment of block-based image coding systems such as the JPEG coding standard and vector quantization-based coding algorithms, The principle of the proposed scheme can also be applied to block-based video compression schemes such as the H.261, MPEG, and HDTV standards, Simulation results are presented to illustrate the effectiveness of the proposed method,

关键词： image coding Fuzzy logic Transform coding Interpolation Extrapolation Surface texture Fuzzy reasoning Fuzzy sets Inference algorithms Video compression

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Novel vector quantization based algorithms for low-power image coding and decoding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING 1999年第2期46卷 193-198页

作者： Masselos, K Merakos, P Stouraitis, T Goutis, CE Univ Patras Dept Elect & Comp Engn Rio 26500 Greece

In this paper, a novel scheme for low-power image coding and decoding based on vector quantization is presented. The proposed scheme uses small codebooks, and block transformations are applied to the codewords during coding. Using small codebooks, the proposed scheme has reduced memory requirements in comparison to classical vector quantization. The transformations applied to the codewords extend computationally the small codebooks compensating for the quality degradation introduced by the small codebook size. Thus the coding task becomes computation-based rather than memory-based, leading to significant power savings since memory-related power consumption forms the major part of the total power consumption of a system. Since the parameters or the transformations depend on the image block under coding, the small codebooks are dynamically adapted to the specific block under coding leading to acceptable image qualities. The proposed scheme leads to power savings of a factor of 10 in coding and of a factor of 3 in decoding, at least in comparison to classical full-search vector quantization. The main factor affecting both image quality and power consumption is the size of the codebook that is used.

关键词： Vector quantization image coding Decoding Energy consumption Computational complexity image quality Video coding Video compression Degradation Computer displays

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image coding Based on image Segmentation and Shape Adaptive All Phase Biorthogonal Transform

Image Coding Based on Image Segmentation and Shape Adaptive ...

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IEEE International Conference on Imaging Systems and Techniques (IST)

作者： Jiang, Baochen Yang, Aiping Wang, Chengyou Hou, Zhengxin Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

ISBN: (纸本)9781467357913

Based on the shape adaptive discrete cosine transform (SA-DCT) and its application in image coding, this paper proposes a new image coding algorithm based on image segmentation and shape adaptive all phase biorthogonal transform (SA-APBT). In this paper, an image is divided into two parts, the region-of-interest (ROI) and the background area, which can be encoded separately. Intra macroblocks (all pixels are located in ROI or background region) are processed with APBT;while marginal macroblocks (part of pixels are located in ROI) are processed with SA-APBT. Experimental results are obtained with the test images. It can be concluded that the coding performance of the proposed algorithm is better than that of conventional algorithms. Both the objective quality and subjective effect are improved.

关键词： image coding image segmentation region-of-interest (ROI) shape adaptive discrete cosine transform (DCT) all phase biorthogonal transform (APBT)

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MOTION COMPENSATED image-coding WITH A COMBINED MAXIMUM APOSTERIORI AND REGRESSION ALGORITHM

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IEEE TRANSACTIONS ON COMMUNICATIONS 1985年第8期33卷 844-857页

作者： BOWLING, CD JONES, RA UNIV ARKANSAS DEPT ELECT ENGN FAYETTEVILLE AR 72701 USA

In this paper we present a method for motion compensated image coding based upon a two-step displacement estimation procedure. The first step utilizes a maximum a posteriori (MAP) estimator to determine the best integer displacement, while the second step requires solving for the regression coefficients that supply the same information as the noninteger portion of the displacement. This approach is a different and simplified procedure in that the integer displacement is measured first, and then a linear combination of only four values from the previous image, shifted by the measured integer displacement, is used. This procedure differs both from the ones which measure the displacement vector D first and interpolate the previous image, and from the ones which use only linear prediction. This method is derived and results are presented for two separate 40-frame digital image sequences. A sum of absolute error distortion measure is used to determine the optimal structure of the residual quantizer.

关键词： image coding Displacement measurement Distortion measurement Signal Processing Society Acoustic signal processing Motion estimation Vectors Digital images Hardware Pixel

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Semantic Structured image coding Framework for Multiple Intelligent Applications

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第9期31卷 3631-3642页

作者： Sun, Simeng He, Tianyu Chen, Zhibo Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230026 Peoples R China DAMO Acad Alibaba Grp Hangzhou 311121 Peoples R China

Fast-growing intelligent media processing applications demand efficient processing throughout the processing chain from the edge to the cloud, and the complexity bottleneck usually lies in the parallel decoding of multiple-channel compressed bitstreams before analyzing. This occurs because the traditional media coding scheme generates a binary stream without a semantic structure, which is unable to be operated directly at the bitstream level to support different tasks such as classification, recognition, detection, etc. Therefore, in this article, we propose a learning-based semantically structured image coding (SSIC) framework to generate a semantically structured bitstream (SSB), where each part of the bitstream represents a specific object and can be directly used for the aforementioned intelligent tasks. Specifically, we integrate an object location extraction module into the compression framework to locate and align objects in the feature domain. Then, each object together with the background is compressed separately and reorganized to form a structured bitstream to enable the analysis or reconstruction of specific objects directly from partial bitstream. Furthermore, in contrast to existing learning-based compression schemes that train the specific model for a specific bitrate, we share most of the model parameters among various bitrates to significantly reduce the model size for variable-rate compression. The experimental results demonstrate the effectiveness of the proposed coding scheme whose compression performance is comparable to existing image coding schemes, where intelligent tasks such as classification and pose estimation can be directly performed on a partial bitstream without performance degradation, significantly reducing the complexity for analyzing tasks.

关键词： image coding Task analysis Bit rate image reconstruction Amplitude modulation Semantics Computational modeling Compression semantically structured compression neural networks

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General construction of wavelet filters via a lifting scheme and its application in image coding

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OPTICAL ENGINEERING 2003年第7期42卷 1949-1955页

作者： Liang, DL Cheng, LH Zhang, ZH Natl Univ Def Technol Sch Elect Engn & Sci Changsha 410073 Peoples R China

By using a lifting scheme, we present a technique to construct compactly supported wavelets whose coefficients are composed of free variables that lie in an interval. By selecting the coefficients of the 9 to 7 wavelet filter and associated lifting scheme, an efficient approach via wavelets for image compression is developed. Furthermore, the rationalized coefficients wavelet filter that can be implemented with simple integer arithmetic is achieved, and its characteristic is close to the well-known original irrational coefficients 9 to 7 wavelet filters developed by Cohen, Daubechies, and Feauveau. Software and hardware simulations show that the new method has very low complexity, and simultaneously preserves a high quality of a compressed image. (C) 2003 Society of Photo-Optical Instrumentation Engineers.

关键词： wavelets transforms lifting scheme image coding

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