[go: up one dir, main page]

Nagaraj et al., 2020 - Google Patents

Competent ultra data compression by enhanced features excerption using deep learning techniques

Nagaraj et al., 2020

Document ID
12080771480572286565
Author
Nagaraj P
Rao J
Muneeswaran V
Kumar A
et al.
Publication year
Publication venue
2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS)

External Links

Snippet

The objective of data compression is to extract the main features of the data and to restore the decompressed data from latent space ie, compressed data without any quality or noise. In this paper, a Convolutional LSTM model is proposed to reduce the redundancy data and …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
    • G06T9/008—Vector quantisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding, e.g. from bit-mapped to non bit-mapped
    • G06T9/001—Model-based coding, e.g. wire frame
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals
    • H04N19/94—Vector quantisation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object

Similar Documents

Publication Publication Date Title
Nagaraj et al. Competent ultra data compression by enhanced features excerption using deep learning techniques
KR102332490B1 (en) Compression methods, chips, electronics and media for deep neural networks
Ballé et al. Integer networks for data compression with latent-variable models
US12058333B1 (en) System and methods for upsampling of decompressed data after lossy compression using a neural network
US20220164995A1 (en) A method, an apparatus and a computer program product for video encoding and video decoding
KR20210135465A (en) Computer system for apparatus for compressing trained deep neural networks
US20250193439A1 (en) Image series transformation for optimal compressibility with neural upsampling
KR102368447B1 (en) Compressing apparatus and method of trained deep artificial neural networks for video coding
CN117616753A (en) Video compression using optical flow
CN115022637A (en) Image coding method, image decompression method and device
US20250192798A1 (en) System and methods for upsampling of decompressed correlated multichannel data using a neural network
Mishra et al. Multi-scale network (MsSG-CNN) for joint image and saliency map learning-based compression
CN113382244B (en) Coding and decoding network structure, image compression method, device and storage medium
Kulkarni et al. Image Denoising using Autoencoders: Denoising noisy imgaes by removing noisy pixels/grains from natural images using Deep learning and autoencoders techniques
KR20230162061A (en) Multi-rate neural networks for computer vision tasks in the compressed domain.
US20250055475A1 (en) System and method for multi-type data compression or decompression with a virtual management layer
CN117795532A (en) Online training of computer vision task models in compressed domains
Mondal et al. Optimized lossless audio compression using DCT energy thresholding and machine learning technique
KR20210119046A (en) Apparatus and method for compressing trained deep neural networks for multimedia contents processing
US20250322223A1 (en) Method and system for data compression using state space neural networks
US20250192799A1 (en) System and methods for upsampling of decompressed time-series data using a neural network
US20250191231A1 (en) System and methods for upsampling of decompressed data after lossy compression using a neural network
US20250191595A1 (en) System and methods for upsampling of decompressed speech data using a neural network
CN120692401B (en) Remote sensing image compression method and device based on frequency domain enhancement and adaptive optimization
US20250096815A1 (en) System and method for multi-type data compression or decompression with a virtual management layer