Pulse or digital communications – Bandwidth reduction or expansion – Television or motion video signal
Reexamination Certificate
2000-04-17
2003-07-29
Le, Vu (Department: 2613)
Pulse or digital communications
Bandwidth reduction or expansion
Television or motion video signal
C375S240080
Reexamination Certificate
active
06600786
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to the compression of video data, and more particularly to a synchronized encoder and smart decoder system for the efficient transmittal and storage of motion video data.
BACKGROUND OF THE INVENTION
1. Brief Introduction
As consumers desire more video-intensive modes of communications, the limited bandwidth of current transmission modes (e.g., broadcast, cable, telephone lines, etc.) becomes prohibitive. The introduction of the Internet, and the subsequent popularity of the world wide web, video conferencing, and digital & interactive television require more efficient ways of utilizing existing bandwidth. Further, video-intensive applications require immense storage capacity. The advent of multi-media capabilities on most computer systems have taxed traditional storage devices, such as hard drives, to the limit.
Compression allows digital motion video to be represented efficiently and cheaply. The benefit of compression is that it allows more information to be transmitted in a given amount of time, or stored in a given storage medium. The ultimate goal of video compression is to reduce the bitstream, or video information flow, of the video sequences as much as possible, while retaining enough information that the decoder or receiver can reconstruct the video image sequences in a manner adequate for the specific application, such as television, videoconferencing, etc.
Most digital signals contain a substantial amount of redundant, superfluous information. For example, a stationary video scene produces nearly identical images in each scene. Most video compression routines attempt to remove the superfluous information so that the related image frames can be represented in terms of previous image frame(s), thus eliminating the need to transmit the entire scene of each video frame. Alternatively, routines like motion JPEG, code each video frame separately and ignore temporal redundancy./
2. Previous Attempts
There have been numerous attempts at adequately compressing video imagery. These methods generally fall into the following two categories: 1) spatial redundancy reduction, and 2) temporal redundancy reduction.
2.1 Spatial Redundancy Reduction
The first type of video compression focuses on the reduction of spatial redundancy, i.e., taking advantage of the correlation among neighboring pixels in order to derive a more efficient representation of the important information in an image frame. These methods are more appropriately termed still-image compression routines, as they work reasonably well on individual video image frames but do not attempt to address the issue of temporal, or frame-to-frame, redundancy, as explained in Section 2.2. Common still-image compression schemes include JPEG, wavelets, and fractals.
2.1.1 JPEG/DCT Based Image Compression
One of the first commonly used methods of still-image compression was the direct cosine transformation (“DCT”) compression system, which is at the heart of JPEG.
DCT operates by representing each digital image frame as a series of cosine waves or frequencies. Afterwards, the coefficients of the cosine series are quantized. The higher frequency coefficients are quantized more harshly than those of the lower frequencies. The result of the quantization is a large number of zero coefficients, which can be encoded very efficiently. However, JPEG and similar compression schemes do not address the crucial issue of temporal redundancy.
2.1.2 Wavelets
As a slight improvement to the DCT compression scheme, the wavelet transformation compression scheme was devised. This system is similar to the DCT, differing mainly in that an image frame is represented as a series of wavelets, or windowed oscillations, instead of as a series of cosine waves.
2.1.3 Fractals
Another technique is known as fractal compression. The goal of fractal compression is to take an image and determine a single function, or a set of functions, which fully describe(s) the image frame. A fractal is an object that is self-similar at different scales or resolutions, i.e., no matter what resolution one looks at, the object remains the same. In theory, where fractals allow simple equations to describe complex images, very high compression ratios shall be achievable.
Unfortunately, fractal compression is not a viable method of general compression. The high compression ratios are only achievable for specially constructed images, and only with considerable help from a person guiding the compression process. In addition, fractal compression is very computationally intensive.
2.2 Temporal and Spatial Redundancy Reduction
Adequate motion video compression requires reduction of both temporal and spatial redundancies within the sequence of frames that comprise video. Temporal redundancy removal is concerned with the removal from the bitstream of information that has already been coded in previous image frames. Block matching is the basis for most currently used effective means of temporal redundancy removal.
2.2.1 Block-Based Motion Estimation
In block matching, the image is subdivided into uniform size blocks (more generally, into polygons), and each block is tracked from one frame to another and represented by a motion vector, instead of having the block re-coded and placed into the bitstream for a second time. Examples of compression routines that use block matching include MPEG, and variants thereof.
MPEG encodes the first frame in a sequence of related frames in its entirety as a so-called intra-frame, or I-frame. An I-frame is a type of key frame, meaning an image frame which is completely self-contained and not described in relation to any other image frame. To create an I-frame, MPEG performs a still-image compression on the first frame, including dividing the frame into 16 pixel by 16 pixel square blocks. Other (so-called “predicted”) frames are encoded with respect to the I-frame by predicting corresponding blocks of the other frame in relation to that of the I-frame. That is, MPEG attempts to find each block of an I-frame within the other frame. For each block that still exists in the other frame, MPEG transmits the motion vector, or movement, of the block along with block identifying information. However, as a block moves from frame to frame, it may change slightly. The difference relative to the I-frame is known as residue. Additionally, as blocks move, previously hidden areas may become visible for the first time. These previously hidden areas are also known as residue. That is, the collective remaining information after the block motion is sent is known as the residue, which is coded using JPEG and sent to the receiver to complete the image frame.
Subsequent frames are predicted with respect to either the blocks of the I-frame or a preceding predicted frame. In addition, the prediction can be bi-directional, i.e., with reference to both preceding and subsequent I-frames or predicted frames. The prediction process continues until a new key frame is inserted, at which point a new I-frame is encoded and the process repeats itself.
Although state of the art, block matching is highly inefficient and fails to take advantage of the known general physical characteristics or other information inherent in the images. The block method is both arbitrary and crude, as the blocks do not have any relationship with real objects in the image. A given block may comprise a part of an object, a whole object, or even multiple dissimilar objects with unrelated motion. In addition, neighboring objects will often have similar motion. However, since blocks do not correspond to real objects, block-based systems cannot use this information to further reduce the bitstream.
Yet another major limitation of block-based matches arises because the residue created by block-based matching is generally noisy and patchy. Thus, block-based residues do not lend themselves to good compression via standard image compression schemes such as DCT, wavelets, or fractals.
2.3 Alternatives
It is well recognized that the state of the art needs improvement,
Prakash Adityo
Prakash Eniko F.
Albert Philip H.
Le Vu
Pulsent Corporation
Townsend and Townsend / and Crew LLP
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